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An Evolutionary Approach to Synthetic Biology,
Zen and the Art of Creating Life
Thomas S. Ray
ATR Human Information Processing Research Laboratories
2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-02, Japan
ray@hip.atr.co.jp, ray@udel.edu
October 21, 1993
Ray, T. S. In press. An evolutionary approach to synthetic biology,
Zen and the art of creating life. Artificial Life 1(1): xx--xx. MIT Press.
Abstract
Our concepts of biology, evolution and complexity are constrained
by having observed only a single instance of life, life on Earth.
A truly comparative biology is needed to extend these concepts.
Because we can not observe life on other planets, we are left with
the alternative of creating artificial life forms on Earth. I will
discuss the approach of inoculating evolution by natural selection
into the medium of the digital computer. This is not a
physical/chemical medium, it is a logical/informational medium.
Thus these new instances of evolution are not subject to the same
physical laws as organic evolution (e.g., the laws of thermodynamics),
and therefore exist in what amounts to another universe, governed by
the ``physical laws'' of the logic of the computer. This exercise
gives us a broader perspective on what evolution is and what it does.
An evolutionary approach to synthetic biology consists of inoculating
the process of evolution by natural selection into an artificial medium.
Evolution is then allowed to find the natural forms of living organisms
in the artificial medium. These are not models of life, but independent
instances of life. This essay is intended to communicate a way of
thinking about synthetic biology that leads to a particular approach:
to understand and respect the natural form of the artificial medium, to
facilitate the process of evolution in generating forms that are adapted
to the medium, and to let evolution find forms and processes that
naturally exploit the possibilities inherent in the medium. Examples
are cited of synthetic biology embedded in the computational medium,
where in addition to being an exercise in experimental comparative
evolutionary biology, it is also a possible means of harnessing the
evolutionary process for the production of complex computer software.
Contents
1) Synthetic Biology
2) Recognizing Life
3) What Natural Evolution Does
3.1) Evolution in Sequence Space
3.2) Natural Evolution in an Artificial Medium
4) The Approach
5) The Computational Medium
6) The Genetic Language
7) Genetic Operators
7.1) Mutations
7.2) Flaws
7.3) Recombination --- Sex
7.3.1) The Nature of Sex
7.3.2) Implementation of Digital Sex
7.4) Transposons
8) Artificial Death
9) Operating System
10) Spatial Topology
11) Ecological Context
11.1) The Living Environment
11.2) Diversity
11.3) Ecological Attractors
12) Cellularity
13) Multi-Cellularity
13.1) Biological Perspective --- Cambrian Explosion
13.2) Computational Perspective --- Parallel Processes
13.3) Evolution as a Proven Route
13.4) Fundamental Definition
13.5) Computational Implementation
13.6) Digital ``Neural Networks'' --- Natural Artificial Intelligence
14) Digital Husbandry
15) Living Together
16) Challenges
16.1) Respecting the Medium
16.2) Understanding Evolvability
16.3) Creating Organized Sexuality
16.4) Creating Multi-cellularity
16.5) Controlling Evolution
16.6) Living Together
Acknowledgements
Bibliography
1. Synthetic Biology
Artificial Life (AL) is the enterprise of understanding biology by
constructing biological phenomena out of artificial components, rather
than breaking natural life forms down into their component parts. It
is the synthetic rather than the reductionist approach. I will
describe an approach to the synthesis of artificial living forms
that exhibit natural evolution.
The umbrella of Artificial Life is broad, and covers three principal
approaches to synthesis: in hardware (e.g., robotics, nanotechnology),
in software (e.g., replicating and evolving computer programs),
in wetware (e.g., replicating and evolving organic molecules, nucleic
acids or others). This essay will focus on software synthesis, although
it is hoped that the issues discussed will be generalizable to any synthesis
involving the process of evolution.
I would like to suggest that software syntheses in AL could be divided
into two kinds: simulations and instantiations of life processes. AL
simulations represent an advance in biological modeling, based on a
bottom-up approach, that has been made possible by the increase of
available computational power. In the older approaches to modeling of
ecological or evolutionary phenomena, systems of differential equations
were set up that expressed relationships between covarying quantities
of entities (i.e., genes, alleles, individuals, or species) in the
populations or communities.
The new bottom up approach creates a population of data structures, with
each instance of the data structure corresponding to a single entity.
These structures contain variables defining the state of an individual.
Rules are defined as to how the individuals interact with one another and
with the environment. As the simulation runs, populations of these
data structures interact according to local rules, and the global behavior
of the system emerges from those interactions. Several very good examples
of bottom up ecological models have appeared in the AL literature
( Hoge, Tayl ). However, ecologists have also developed this same
approach independently of the AL movement, and have called the approach
``individual based'' models ( DeAn, Hust88 ).
The second approach to software synthesis is what I have called
instantiation rather than simulation. In simulation, data structures
are created which contain variables that represent the states of the
entities being modeled. The important point is that in simulation,
the data in the computer is treated as a representation of something
else, such as a population of mosquitoes or trees. In instantiation,
the data in the computer does not represent anything else. The data
patterns in an instantiation are considered to be living forms in their
own right, and are not models of any natural life form. These can
from the basis of a comparative biology ( MaSm92 ) .
The object of an AL instantiation is to introduce the natural form and
process of life into an artificial medium. This results in an artificial
life form in some medium other than carbon chemistry, and is not a model
of organic life forms. The approach discussed in this essay involves
introducing the process of evolution by natural selection into the
computational medium. I consider evolution to be the fundamental
process of life, and the generator of living form.
2. Recognizing Life
Most approaches to defining life involve assembling a short list of
properties of life, and then testing candidates on the basis of
whether or not they exhibit the properties on the list. The main
problem with this approach is that there is disagreement as to what
should be on the list. My private list contains only two items:
self-replication and open-ended evolution. However, this reflects
my biases as an evolutionary biologist.
I prefer to avoid the semantic argument and take a different approach
to the problem of recognizing life. I was led to this view by
contemplating how I would regard a machine that exhibited conscious
intelligence at such a level that it could participate as an equal
in a debate such as this. The machine would meet neither of my two
criteria as to what life is, yet I don't feel that I could deny that
the process it contained was alive.
This means that there are certain properties that I consider to be
unique to life, and whose presence in a system signify the existance
of life in that system. This suggests an alternative approach to the
problem. Rather than creating a short list of minimal requirements
and testing whether a system exhibits all items on the list, create a
long list of properties unique to life and test whether a system
exhibits any item on the list.
In this softer, more pluralistic approach to recognizing life, the
objective is not to determine if the system is alive or not, but to
determine if the system exhibits a ``genuine'' instance of some
property that is a signature of living systems (e.g., self-replication,
evolution, flocking, consciousness).
Whether we consider a system living because it exhibits some property that
is unique to life amounts to a semantic issue. What is more important is
that we recognize that it is possible to create disembodied but genuine
instances of specific properties of life in artificial systems. This
capability is a powerful research tool. By separating the property of
life that we choose to study, from the many other complexities of natural
living systems, we make it easier to manipulate and observe the property
of interest. The objective of the approach advocated in this paper is
to capture genuine evolution in an artificial system.
3. What Natural Evolution Does
Evolution by natural selection is a process that enters into
a physical medium. Through iterated replication-with-selection of
large populations through many generations, it searches out the
possibilities inherent in the ``physics and chemistry'' of the
medium in which it is embedded. It exploits any inherent self-organizing
properties of the medium, and flows into natural attractors realizing
and fleshing out their structure.
Evolution never escapes from its ultimate imperative: self-replication.
However, the mechanisms that evolution discovers for achieving this
ultimate goal gradually become so convoluted and complex that the
underlying drive can seem to become superfluous. Some philosophers have
argued that the evolutionary theory as expressed by the phrase ``survival
of the fittest'' is tautological, in that the fittest are defined as
those that survive to reproduce. In fact, fitness is achieved through
innovation in engineering of the organism ( Sobe ). However there
remains something peculiarly self-referential about the whole enterprise.
There is some sense in which life may be a natural tautology.
Evolution is both a defining characteristic and the creative process
of life itself. The living condition is a state that complex physical
systems naturally flow into under certain conditions. It is a
self-organizing, self-perpetuating state of auto-catalytically increasing
complexity. The living component of the physical system quickly becomes
the most complex part of the system, such that it re-shapes the medium,
in its own image as it were. Life then evolves adaptations predominantly
in relation to the living components of the system, rather than the
non-living components. Life evolves adaptations to itself.
3.1 Evolution in Sequence Space
Think of organisms as occupying a ``genotype space'' consisting of
all possible sequences of all possible lengths of the
elements of the genetic system (i.e., nucleotides or machine instructions).
When the first organism begins replicating, a single self-replicating
creature, with a single sequence of a certain length occupies a single
point in the genotype space. However, as the creature replicates in the
environment, a population of creatures forms, and errors cause genetic
variation, such that the population will form a cloud of points in the
genotype space, centered around the original point.
Because the new genotypes that form the cloud are formed by random
processes, most of them are completely inviable, and die without
reproducing. However, some of them are capable of reproduction. These
new genotypes persist, and as some of them are affected by mutation, the
cloud of points spreads further. However, not all of the viable genomes
are equally viable. Some of them discover tricks to replicate more
efficiently. These genotypes increase in frequency, causing the population
of creatures at the corresponding points in the genotype space to increase.
Points in the genotype space occupied by greater populations of
individuals will spawn larger numbers of mutant offspring, thus the density
of the cloud of points in the genotype space will shift gradually in the
direction of the more fit genotypes. Over time, the cloud of points will
percolate through the genotype space, either expanding outward as a result
of random drift, or by flowing along fitness gradients.
Most of the volume of this space represents completely inviable sequences.
These regions of the space may be momentarily and sparsely occupied by
inviable mutants, but the cloud will never flow into the inviable regions.
The cloud of genotypes may bifurcate as it flows into habitable regions
in different directions, and it may split as large genetic changes spawn
genotypes in distant but viable regions of the space. We may imagine that
the evolving population of creatures will take the form of wispy clouds
flowing through this space.
Now imagine for a moment the situation that there were no selection.
This implies that every sequence is replicated at an equal rate. Mutation
will cause the cloud of points to expand outward, eventually filling the
space uniformly. In this situation, the complexity of the structure of
the cloud of points does not increase through time, only the volume that
it occupies. Under selection by contrast, through time the cloud will
take on an intricate structure as it flows along fitness gradients and
percolates by drift through narrow regions of viability in a largely
uninhabitable space.
Consider that the viable region of the genotype space is a very small
subset of the total volume of the space, but that it probably exhibits
a very complex shape, forming tendrils and sheets sparsely permeating
the otherwise empty space. The complex structure of this cloud can be
considered to be a product of evolution by natural selection. This
thought experiment appears to imply that the intricate structure that
the cloud of genotypes may assume through evolution is fully deterministic.
Its shape is pre-defined by the physics and chemistry and the structure of
the environment, in much the same way that the form of the Mandlebrot set
is pre-determined by its defining equation. The complex structure of this
viable space is inherent in the medium, and is an example of ``order
for free'' ( Kauf ).
No living world will ever fill the entire viable subspace, either at a
single moment of time, or even cumulatively over its entire history. The
region actually filled will be strongly influenced by the original
self-replicating sequence, and by stochastic forces which will by chance
push the cloud down a subset of possible habitable pathways. Furthermore,
co-evolution and ecological interactions imply that certain regions can
only be occupied when certain other regions are also occupied. This
concept of the flow of genotypes through the genotype space is essentially
the same as that discussed by Eigen ( Eige ) in the context of
``quasispecies''. Eigen limited his discussion to species of viruses,
where it is also easy to think of sequence spaces. Here, I am extending
the concept beyond the bounds of the species, to include entire phylogenies
of species.
3.2 Natural Evolution in an Artificial Medium
Until recently, life has been known as a state of matter, particularly
combinations of the elements carbon, hydrogen, oxygen, nitrogen and
smaller quantities of many others. However, recent work in the field
of Artificial Life has shown that the natural evolutionary process can
proceed with great efficacy in other media, such as the informational
medium of the digital computer ( Adam, BaDa, Broo, Davi1, Davi2, DeGr,
Fefe, Gray, Kamp1, Kamp2, Lith, Male, Mano, Rasm90, Rasm91, Ray91a,
Ray91b, Ray91c, Ray91d, RayIp, RaySu, Skip, Surk, Tack )
These new natural evolutions, in artificial media, are beginning
to explore the possibilities inherent in the ``physics and chemistry''
of those media. They are organizing themselves and constructing
self-generating complex systems. While these new living systems are
still so young that they remain in their primordial state, it appears
that they have embarked on the same kind of journey taken by life on earth,
and presumably have the potential to evolve levels of complexity that
could lead to sentient and eventually intelligent beings.
If natural evolution in artificial media leads to sentient or intelligent
beings, they will likely be so alien that they will be difficult
to recognize. The sentient properties of plants are so radically
different from those of animals, that they are generally unrecognized
or denied by humans, and plants are merely in another kingdom of the
one great tree of organic life on earth ( Ray79, Ray92, StRa ).
Synthetic organisms evolving in other media such as the digital
computer, are not only not a part of the same phylogeny, but they are
not even of the same physics. Organic life is based on conventional
material physics, whereas digital life exists in a logical, not
material, informational universe. Digital intelligence will likely
be vastly different from human intelligence; forget the Turing test.
4. The Approach
Marcel, a mechanical chessplayer... his exquisite 19th-century brainwork
- the human art it took to build which has been flat lost, lost as the
dodo bird ... But where inside Marcel is the midget Grandmaster, the
little Johann Allgeier? where's the pantograph, and the magnets? Nowhere.
Marcel really is a mechanical chessplayer. No fakery inside to give
him any touch of humanity at all.
--- Thomas Pynchon, Gravity's Rainbow.
The objective of the approach discussed here, is to create an
instantiation of evolution by natural selection in the computational
medium. This creates a conceptual problem that requires considerable
art to solve: ideas and techniques must be learned by studying organic
evolution, and then applied to the generation of evolution in a digital
medium, without forcing the digital medium into an ``un-natural''
simulation of the organic world.
We must derive inspiration from observations of organic life, but we
must never lose sight of the fact that the new instantiation is not
organic, and may differ in many fundamental ways. For example,
organic life inhabits a Euclidean space, however computer memory is
not a Euclidean space. Inter-cellular communication in the organic
world is chemical in nature, and therefore a single message generally
can pass no more information than on or off. By contrast,
communication in digital computers generally involves the passing of
bit patterns, which can carry much more information.
The fundamental principal of the approach being advocated here is
to understand and respect the natural form of the digital computer,
to facilitate the process of evolution in generating forms that are
adapted to the computational medium, and to let evolution find forms
and processes that naturally exploit the possibilities inherent in the
medium .
Situations arise where it is necessary to make significant changes from
the standard computer architecture. But such changes should be
made with caution, and only when there is some feature of standard
computer architectures which clearly inhibits the desired processes.
Examples of such changes are discussed in the section ``The Genetic
Language'' below. Less substantial changes are also discussed in the
sections on the ``Flaw'' genetic operator, ``Mutations'', and
``Artificial Death''. The sections on ``Spatial Topology'' and
``Digital `Neural Networks' --- Natural AI'' are little tirades against
examples of what I consider to be un-natural transfers of forms from
the natural world to the digital medium.
5. The Computational Medium
The computational medium of the digital computer is an informational
universe of boolean logic, not a material one. Digital organisms live
in the memory of the computer, and are powered by the activity of the
central processing unit (CPU). Whether the hardware of the CPU and
memory is built of silicon chips, vacuum tubes, magnetic cores, or
mechanical switches is irrelevant to the digital organism. Digital
organisms should be able to take on the same form in any computational
hardware, and in this sense are ``portable'' across hardware.
Digital organisms might as well live in a different universe from
us, as they are not subject to the same laws of physics and chemistry.
They are subject to the ``physics and chemistry'' of the rules governing
the manipulation of bits and bytes within the computer's memory and CPU.
They never ``see'' the actual material from which the computer is
constructed, they see only the logic and rules of the CPU and the
operating system. These rules are the only ``natural laws'' that
govern their behavior. They are not influenced by the natural laws
that govern the material universe (e.g., the laws of thermodynamics).
A typical instantiation of this type involves the introduction of a
self-replicating machine language program into the RAM memory of a
computer subject to random errors such as bit flips in the memory or
occasionally inaccurate calculations ( BaDa, Broo, DeGr, Male, Ray91a ).
This generates the basic conditions for evolution by natural selection
as outlined by Darwin ( Darw59 ): self-replication in a finite
environment with heritable genetic variation.
In this instantiation, the self-replicating machine language program
is thought of as the individual ``digital organism'' or ``creature''.
The RAM memory provides the physical space that the creatures occupy. The
CPU provides the source of energy. The memory consists of a large array
of bits, generally grouped into eight bit bytes and sixteen or thirty-two
bit words. Information is stored in these arrays as voltage patterns
which we usually symbolize as patterns of ones and zeros.
The ``body'' of a digital organism is the information pattern in memory
that constitutes its machine language program. This information pattern
is data, but when it is passed to the CPU, it is interpreted as a series of
executable instructions. These instructions are arranged in such a way
that the data of the body will be copied to another location of memory.
The informational patterns stored in the memory are altered only through
the activity of the CPU. It is for this reason that the CPU is thought
of as the analog of the energy source. Without the activity of the CPU,
the memory would be static, with no changes in the informational patterns
stored there.
The logical operations embodied in the instruction set of the CPU
constitute a large part of the definition of the ``physics and chemistry''
of the digital universe. The topology of the computer's memory
(discussed below) is also a significant component of the digital
physics. The final component of the digital physics is the operating
system, a software program running on the computer, which embodies
rules for the allocation of resources such as memory space and CPU
time to the various processes running on the computer.
The instruction set of the CPU, the memory, and the operating system
together define the complete ``physics and chemistry'' of the universe
inhabited by the digital organism. They constitute the physical
environment within which digital organisms will evolve. Evolving
digital organisms will compete for access to the limited resources of
memory space and CPU time, and evolution will generate adaptations for
the more agile access to and the more efficient use of these resources.
6. The Genetic Language
The simplest possible instantiation of a digital organism is a
machine language program that codes for self-replication. In this
case, the bit pattern that makes up the program is the body of the
organism, and at the same time its complete genetic material.
Therefore, the machine language defined by the CPU constitutes the
genetic language of the digital organism.
It is worth noting at this point that the organic organism most
comparable to this kind of digital organism is the hypothetical,
and now extinct, RNA organism ( Benn ). These were presumably nothing
more than RNA molecules capable of catalyzing their own replication.
What the supposed RNA organisms have in common with the simple
digital organism is that a single molecule constitutes the body
and the genetic information, and effects the replication. In the
digital organism a single bit pattern performs all the same functions.
The use of machine code as a genetic system raises the problem of
brittleness. It has generally been assumed by computer scientists
that machine language programs can not be evolved because random
alterations such as bit flips and recombinations will always produce
inviable programs. It has been suggested ( FaBe ) that overcoming
this brittleness and ``Discovering how to make such self-replicating
patterns more robust so that they evolve to increasingly more complex
states is probably the central problem in the study of artificial life.''
The assumption that machine languages are too brittle to evolve is
probably true, as a consequence of the fact that machine languages
have not previously been designed to survive random alterations.
However, recent experiments have shown that brittleness can be
overcome by addressing the principal causes, and without fundamentally
changing the structure of machine languages ( Ray91a, RaySu ).
The first requirement for evolvability is graceful error handling.
When code is being randomly altered, every possible meaningless or
erroneous condition is likely to occur. The CPU should be designed
to handle these conditions without crashing the system. The simplest
solution is for the CPU to perform no operation when it meets
these conditions, perhaps setting an error flag, and to proceed to
the next instruction.
Due to random alterations of the bit patterns, all possible bit patterns
are likely to occur. Therefore a good design is for all possible bit
patterns to be interpretable as meaningful instructions by the CPU.
For example in the Tierra system ( Ray91a, Ray91b, Ray91c, Ray91d,
RayIp, RaySu ), a five bit instruction set was chosen, in which all
thirty-two five bit patterns represent good machine instructions.
This approach (all bit patterns meaningful) also could imply a lack of syntax,
in which each instruction stands alone, and need not occur in the company
of other instructions. To the extent that the language includes syntax,
where instructions must precede or follow one another in certain orders,
random alterations are likely to destroy meaningful syntax thereby making
the language more brittle. A certain amount of this kind of brittleness
can be tolerated as long as syntax errors are also handled gracefully.
During the design of the first evolvable machine language ( Ray91a ),
a standard machine language (Intel 80X86) was compared to the genetic
language of organic life, to attempt to understand the difference between
the two languages that might contribute to the brittleness of the former
and the robustness of the latter. One of the outstanding differences
noted was in the number of basic informational objects contained in the
two.
The organic genetic language is written with an alphabet consisting
of four different nucleotides. Groups of three nucleotides form
sixty-four ``words'' (codons), which are translated into twenty
amino-acids by the molecular machinery of the cell. The machine
language is written with sequences of two voltages (bits) which
we conceptually represent as ones and zeros. The number of bits that
form a ``word'' (machine instruction) varies between machine
architectures, and in some architectures is not constant. However,
the number required generally ranges from sixteen to thirty-two. This
means that there are from tens of thousands to billions of machine
instruction bit patterns, which are translated into operations
performed by the CPU.
The thousands or billions of bit patterns that code for machine
instructions contrasts with the sixty four nucleotide patterns that
code for amino acids. The sixty-four nucleotide patterns are degenerate,
in that they code for only twenty amino-acids. Similarly, the machine
codes are degenerate, in that there are at most hundreds rather than
thousands or billions of machine operations.
The machine codes exhibit a massive degeneracy (with respect to
actual operations) as a result of the inclusion of data into the
bit patterns coding for the operations. For example, the add
operation will take two operands, and produce as a result the sum
of the two operands. While there may be only a single add operation,
the instruction may come in several forms depending on where the
values of the two operands come from, and where the resultant sum
will be placed. Some forms of the add instruction allow the
value(s) of the operand(s) to be specified in the bit pattern of
the machine code.
The inclusion of numeric operands in the machine code is the primary
cause of the huge degeneracy. If numeric operands are not allowed,
the number of bit patterns required to specify the complete set of
operations collapses to at most a few hundred.
While there is no empirical data to support it, it is suspected that
the huge degeneracy of most machine languages may be a source of
brittleness. The logic of this argument is that mutation causes
random swapping among the fundamental informational objects, codons
in the organic language, and machine instructions in the digital
language. It seems more likely that meaningful results will be
produced when swapping among sixty-four objects than when swapping
among billions of objects.
The size of the machine instruction set can be made comparable to
the number of codons simply by eliminating numeric operands embedded
in the machine code. However, this change creates some new problems.
Computer programs generally function by executing instructions located
sequentially in memory. However, in order to loop or branch, they
use instructions such as ``jump'' to cause execution to jump to some
other part of the program. Since the locations of these jumps are
usually fixed, the jump instruction will generally have the target
address included as an operand embedded in the machine code.
By eliminating operands from the machine code, we generate the need
for a new mechanism of addressing for jumps. To resolve this problem,
an idea can be borrowed from molecular biology. We can ask the
question: how do biological molecules address one another? Molecules
do not specify the coordinates of the other molecules they
interact with. Rather, they present shapes on their surfaces that are
complementary to the shapes on the surfaces of the target molecules.
The concept of complementarity in addressing can be introduced to
machine languages by allowing the jump instruction to be followed by
some bit pattern, and having execution jump to the nearest occurrence
of the complementary bit pattern.
In the development of the Tierran language,
two changes were introduced to the machine language to reduce
brittleness: elimination of numeric operands from the code, and the
use of complementary patterns to control addressing. The resulting
language proved to be evolvable ( Ray91a ). As a result, nothing
was learned about evolvability, because only one language was tested,
and it evolved. It is not known what features of the language
enhance its evolvability, which detract, and which do not affect
evolvability. Subsequently, three additional languages were tested
and the four languages were found to vary in their patterns and
degree of evolvability ( RaySu ). However, it is still not known
how the features of the language affect its evolvability.
7. Genetic Operators
In order for evolution to occur, there must be some genetic variation
among the offspring. In organic life, this is insured by natural
imperfections in the replication of the informational molecules.
However, one way in which digital ``chemistry'' differs from organic
chemistry is in the degree of perfection of its operations. In the
computer, the genetic code can be reliably replicated without errors
to such a degree that we must artificially introduce errors or other
sources of genetic variation in order to induce evolution.
7.1 Mutations
In organic life, the simplest genetic change is a ``point mutation'',
in which a single nucleic acid in the genetic code is replaced by one
of the three other nucleic acids. This can cause an amino acid
substitution in the protein coded by the gene. The nucleic acid
replacement can be caused by an error in the replication of the DNA
molecule, or it can be caused by the effects of radiation or mutagenic
chemicals.
In the digital medium, a comparably simple genetic change can result
from a bit flip in the memory, where a one is replaced by a zero, or
a zero is replaced by a one. These bit flips can be introduced in a
variety of ways that are analogous to the various natural causes of
mutation. In any case, the bit flips must be introduced at a low to
moderate frequency, as high frequencies of mutation prevent the
replication of genetic information, and lead to the death of the system
( Ray91d ).
Bit flips may be introduced at random anywhere in memory, where they
may or may not hit memory actually occupied by digital organisms.
This could be thought of as analogous to cosmic rays falling at random
and disturbing molecules which may or may not be biological in nature.
Bit flips may also be introduced when information is copied in the
memory, which could be analogous to the replication errors of DNA.
Alternatively, bit flips could be introduced in memory as it is accessed,
either as data or executable code. This could be thought of as damage
due to ``wear and tear''.
7.2 Flaws
Alterations of genetic information are not the only source of noise in
the system. In organic life, enzymes have evolved to increase the
probability of chemical reactions that increase the fitness of the
organism. However, the metabolic system is not perfect. Undesired
chemical reactions do occur, and desired reactions sometimes produce
undesired by-products. The result is the generation of molecular
species that can ``gum up the works'', having unexpected consequences,
generally lowering the fitness of the organism, but possibly raising
it.
In the digital system, an analogue of metabolic (non-genetic) errors
can be introduced by causing the computations carried out by the CPU
to be probabilistic, producing erroneous results at some low frequency.
For example, any time a sum or difference is calculated, the result
could be off by some small value (e.g. plus or minus one). Or, if all
bits are shifted one position to the left or right, an appropriate error
would be to shift by two positions or not at all. When information is
transferred from one location to another, either in the RAM memory or the
CPU registers, it could occasionally be transferred from the wrong
location, or to the wrong location. While flaws do not directly cause
genetic changes, they can cause a cascade of events that result in the
production of an offspring that is genetically different from the parent.
7.3 Recombination --- Sex
7.3.1 The Nature of Sex
In organic life, there are a wide variety of mechanisms by which
offspring are produced which contain genetic material from more
that one parent. This is the sexual process. Recombination
mechanisms range from very primitive and haphazard to elaborately
orchestrated.
At the primitive extreme we find certain species of bacteria, in which
upon death, the cell membrane breaks open, releasing the DNA into the
surrounding medium. Fragments of this dead DNA are absorbed across the
membranes of other bacteria of the same species, and incorporated into
their genome ( Mayn ). This is a one way transferral of genetic
material, rather than a reciprocal exchange.
At the complex extreme we find the conventional sexual system of most of
the higher animals, in which each individual contains two copies of the
entire genome. At reproduction, each of two parents contributes one
complete copy of the genome (half of their genetic material) to the
offspring. This means that each offspring receives one half of its
genetic material from each of two parents, and each parent contributes
one half of its genetic material to each offspring. Very elaborate
behavioral and molecular mechanisms are required to orchestrate this
joint contribution of genetic material to the offspring.
The preponderance of sex remains an enigma to evolutionary theory
( Bell, Ghis, Halv, Hapg, Marg, Mich, Stea, Will ).
Careful analysis has failed to show any benefits from sex, at the level of
the individual organism, that outweigh the high costs (e.g., passing on
only half of the genome). The only obvious benefit of sex is that it
provides diversity among the offspring, allowing the species to adapt more
readily to a changing environment. However, quantitative analysis has
shown that in order for sex to be favored by selection at the individual
level, it is not enough for the environment to change unpredictably, the
environment must actually change capriciously ( Char, MaSm71 ). That is,
whatever genotype has the highest fitness this generation, must have the
lowest fitness the next generation, or at least a trend in this direction,
a negative heritability of fitness.
One theory to explain the perpetuation of sex (based on the Red Queen
hypothesis, see below) states that the environment is in fact capricious,
due to the importance of biotic factors in determining selective forces.
That is, sex is favored because it is necessary to maintain adaptation
in the face of evolving species in the environment (e.g.,
predators/parasites, prey/hosts, competitors) who themselves are
sexual, and can undergo rapid evolutionary change. Predators and
parasites will tend to evolve so as to favor attacking whatever
genotype of their prey/host is the most common. The genotype that
is most successful at present is targeted for future attack. This
dynamic makes the environment capricious in the sense discussed above.
There are fundamental differences in the nature of the evolutionary
process between asexual and sexual organisms. The evolving entity in
an asexual species is a branching lineage of genetic individuals which
retain their genetic identity through the generations. In a sexual
species, the evolving entity is a collective ``gene pool'', and genetic
individuals are absolutely ephemeral, lasting only one generation.
Recalling the discussion of ``genotype space'' above in the section
``Evolution in Sequence Space'',
imagine that we could represent genotype space in two dimensions, and
that we allow a third dimension to represent time. Visualize now, an
evolving asexual organism. Starting with a single individual, it would
occupy a single point in the genotype space at time zero. When
it reproduces, if there is no mutation, its offspring would occupy
the same point in genotype space, at a later time. Thus the lineage of
the asexual organism would appear as a line moving forward in time. If
mutations occur, they cause the offspring to occupy new locations in
genotype space, forming branches in the lineage.
Through time, the evolving asexual lineage would form a tree like
structure in the genotype space--time coordinates. However, every
individual branch of the tree will evolve independently of all the
others. While there may be ecological interactions between genetically
different individuals, there is no exchange of genetic material between
them. From a genetic point of view, each branch of the tree is on its
own; it must adapt, or fail to adapt based on its own genetic resources.
In order to visualize an evolving sexual population we must start with
a population of individuals, each of which will be genetically unique.
Thus they will appear as a scatter of points in the genotype space
plane at time zero. In the next generation, all of the original
genotypes will be dead, however, a completely new set of genotypes will
have been formed from new combinations of pieces of the genomes from
the previous generation. No individual genotypes will survive from
one generation to the next, thus over time, the evolving sexual population
appears as a diffuse cloud of disconnected points, with no lines formed
from persistent genotypes.
The most important distinction between the evolving asexual and sexual
populations is that the asexual individuals are genetically isolated and
must adapt or not based on the limited genetic resources of the individual,
while sexual organisms by comparison draw on the genetic resources of the
entire population, due to the flow of genes resulting from sexual matings.
The entity that evolves in an asexual population is an isolated but
branching lineage of genetic individuals. In a sexual population, the
individual is ephemeral, and the entity that evolves is a ``gene pool''.
Due to the genetic cohesion of a sexual population and the ephemeral
nature of its individuals, the evolving sexual entity exists at a higher
level of organization than the individual organism. The evolving entity,
a gene pool, is supra-organismal. It samples the environment through
many individuals simultaneously, and pools their genetic resources in
finding adaptive genetic combinations.
The definition of the biological species is based on a concept of
sexual reproduction: a group of individuals capable of interbreeding
freely under natural conditions. Species concepts simply do not apply
well to asexual species. In order for synthetic life to be useful
for the study of the properties of species and the speciation process,
it must include an organized sexual process, such that the evolving entity
is a gene pool.
7.3.2 Implementation of Digital Sex
The above discussions of the nature of sexuality are intended to
make the point that it is an important process in
evolutionary biology, and should be included in synthetic implementations
of life. The sexual process is implemented with the ``cross-over''
genetic operator in the field of genetic algorithms, where it has
been considered to be the most important genetic operator ( Holl ).
The cross-over operator has also been implemented in synthetic life
systems ( RayDo, Tack ). However, it has been implemented in
the spirit of a genetic algorithm, rather than in the spirit of
synthetic life. This is because in these implementations the cross-over
process is not under the control of the organism, but rather is forced
on the individual. In addition, these implementations are based on
haploid sex not diploid sex (see below). In order to address many of
the interesting evolutionary questions surrounding sexuality, the sexual
process must be optional, at least through evolution, and should
be diploid.
Primitive sexual processes have appeared spontaneously in the Tierra
synthetic life system ( Ray91a ). However, there apparently has
still not been an implementation of natural organized sexuality in
a synthetic system. I would like to discuss my conception of how
this could be implemented, with particular reference to the Tierra
system.
It would seem that the simplest way of implementing an organized
sexuality that would give rise to an evolving gene pool would involve
the use of ``ploidy''. Ploidy refers to a system in which each
individual contains multiple copies of the complete genome. In the
most familiar sexual system (that used by humans), the gametes
(egg and sperm) contain one copy of the genome (they are haploid),
and all other stages of the life cycle contain two copies (they are
diploid), which derive from the union of a sperm and egg.
In a digital organism whose body consists of a sequence of machine
code, it would be easy to duplicate the sequence and include two
copies within the cell. However, some problems can arise with this
configuration, if the two copies of the genome occupy adjacent
blocks of memory. Which copy of the genome will be executed? When
the organism contributes one of its two copies of the genome to
an offspring, which of the two copies will be contributed, and how
can the mother cell recognize where one complete genome begins and
ends?
A solution to these problems that has been partially implemented in
the Tierra system is to have the two copies of the genome intertwined,
rather than in adjacent blocks of memory. This can be done by letting
alternate bytes represent one genome, and the skipped bytes the other
genome. Tierran instructions utilize only five bits, and so are mapped
to successive bytes in memory. If we instead place successive instructions
in successive sixteen bit words, one copy of the genome can occupy the
high order bytes, and the other genome can occupy the low order bytes
of the words.
This arrangement facilitates relatively simple solutions to the problems
mentioned above. Execution of the genome takes place by having the
instruction pointer execute alternate bytes. In a diploid organism
there are two tracks. The track to initially be executed can be chosen
at random. At a certain frequency, or under certain circumstances, the
executing track can be switched so that both copies of the genome will
be expressed.
Having two parallel tracks helps to resolve the problem of recognizing
where one copy of the genome ends and the other begins, since both genomes
usually begin and end together. Copying of the genome, like execution,
can occur along one track. Optionally, tracks could be switched during
the copy process, to introduce an effect similar to crossing over in
meiosis. In addition, the use of both tracks can be optional, so that
haploid and diploid organisms can coexist in the same soup, and evolution
can favor either form, according to selective pressures.
7.4 Transposons
The explosion of diversity in the Cambrian occurred in the lineage of
the eukaryotes; the prokaryotes did not participate.
One of the most striking genetic differences between eukaryotes and
prokaryotes is that most of the genome of prokaryotes is translated into
proteins, while most of the genome of eukaryotes is not. It has been
estimated that typically 98 of the DNA in eukaryotes is neither
translated into proteins nor involved in gene regulation, that it is
simply ``junk'' DNA ( Thom ). It has been suggested that much of
this junk code is the result of the self-replication of pieces of DNA
within rather than between cells ( DoSa, OrCr ).
Mobile genetic elements, transposons, have this intra-genome
self-replicating property. It has been estimated that 80 of
spontaneous mutations are caused by transposons ( Chao, Gree ).
Repeated sequences, resulting from the activity of mobile elements,
range from dozens to millions in numbers of copies, and from hundreds
to tens of thousands of base pairs in length. They vary widely in
dispersion patterns from clumped to sparse ( JeSc ).
Larger transposons carry one or more genes in addition to those necessary
for transposition. Transposons may grow to include more genes; one
mechanism involves the placement of two transposons into close proximity
so that they act as a single large transposon incorporating the intervening
code. In many cases transposons carry a sequence that acts as a promoter,
altering the regulation of genes at the site of insertion ( Syva ).
Transposons may produce gene products and often are involved in gene
regulation ( DaBr ). However, they may have no effect on the external
phenotype of the individual ( DoSa ). Therefore they evolve through
another paradigm of selection, one that does not involve an external
phenotype. They are seen as a mechanism for the selfish spread of DNA
which may become inactive junk after mutation ( OrCr ).
DNA of transposon origin can be recognized by their palindrome endings
flanked by short non-reversed repeated sequences resulting from
insertion after staggered cuts. In Drosophila melanogaster
approximately 5 to 10 percent of its total DNA is composed of
sequences bearing these signs. There are many families of such
repeated elements, each family possessing a distinctive nucleotide
sequence, and distributed in many sites throughout the genome. One
well known repeated sequence occurring in humans is found to have as
many as a half million copies in each haploid genome ( Stri ).
Elaborate mechanisms have evolved to edit out junk sequences inserted
into critical regions. An indication of the magnitude of the task comes
from the recent cloning of the gene for cystic fibrosis, where it was
discovered that the gene consists of 250,000 base pairs, only 4,440 of
which code for protein, the remainder are edited out of the messenger RNA
before translation ( Kere, Marx, Rior, Romm ).
It appears that many repeated sequences in genomes may have originated
as transposons favored by selection at the level of the gene, favoring
genes which selfishly replicated themselves within the genome. However,
some transposons may have coevolved with their host genome as a result of
selection at the organismal or populational level, favoring transposons
which introduce useful variation through gene rearrangement. It has
been stated that: ``transposable elements can induce mutations that
result in complex and intricately regulated changes in a single step'',
and they are ``A highly evolved macromutational mechanism'' ( Syva ).
In this manner, ``smart'' genetic operators may have evolved, through
the interaction of selection acting at two or more hierarchical levels
(it appears that some transposons have followed another evolutionary
route, developing inter-cellular mobility and becoming viruses
( JeSc ) ). It is likely that transposons today represent the full
continuum from purely parasitic ``selfish DNA'' and viruses to highly
coevolved genetic operators and gene regulators. The possession of
smart genetic operators may have contributed to the explosive
diversification of eukaryotes by providing them with the capacity for
natural genetic engineering.
In designing self replicating digital organisms, it would be worthwhile
to introduce such genetic parasites, in order to facilitate the shuffling
of the code that they bring about. Also, the excess code generated by
this mechanism provides a large store of relatively neutral code that
can randomly explore new configurations through the genetic operations
of mutation and recombination. When these new configurations confer
functionality, they may become selected for.
8. Artificial Death
Death must play a role in any system that exhibits the process of
evolution. Evolution involves a continuing iteration of selection,
which implies differential death. In natural life, death
occurs as a result of accident, predation, starvation, disease,
or if these fail to kill the organism, it will eventually die from
senescence resulting from an accumulation of wear and tear at every
level of the organism including the molecular.
In normal computers, processes are ``born'' when they are initiated
by the user, and ``die'' when they complete their task and
halt. A process whose goal is to repeatedly replicate itself is
essentially an endless loop, and would not spontaneously terminate.
Due to the perfection of normal computer systems, we can not count on
``wear and tear'' to eventually cause a process to terminate.
In synthetic life systems implemented in computers, death is not
likely to be a process that would occur spontaneously, and it must
generally be introduced artificially by the designer. Everyone who
has set up such a system has found their own unique solutions. Todd
( Todd ) recently discussed this problem in general terms.
In the Tierra system ( Ray91a ) death is handled by a ``reaper''
function of the operating system. The reaper uses a linear queue.
When creatures are born, they enter the bottom of the queue. When
memory is full, the reaper frees memory to make space for new creatures
by killing off the top of the queue. However, each time an individual
generates an error condition, it moves up the reaper queue one position.
An interesting variation on this was introduced by Barton-Davis ( BaDa )
who eliminated the reaper queue. In its place, he caused the ``flaw
rate'' (see section on Flaws above) to increase with the age of the
individual, in mimicry of wear and tear. When the flaw rate reached 100 ,
the individual was killed. Skipper ( Skip ) provided a ``suicide''
instruction, which if executed, would cause a process to terminate (die).
The evolutionary objective then became to have a suicide instruction in
your genome which you do not execute yourself, but which you try to get
other individuals to execute. Litherland ( Lith ) introduced death by
local crowding. Davidge caused processes to die when they contained
certain values in their registers ( Davi2 ). Gray ( Gray ) allowed each
process six attempts at reproduction, after which they would die.
9. Operating System
Much of the ``physics and chemistry'' of the digital universe is
determined by the specifications of the operations performed by the
instruction set of the CPU. However, the operating system also
determines a significant part of the physical context. The operating
system manages the allocation of critical resources such as memory
space and CPU cycles.
Digital organisms are processes that spawn processes. As processes
are born, the operating system will allocate memory and CPU cycles
to them, and when they die, the operating system will return the
resources they had utilized to the pool of free resources. In
synthetic life systems, the operating system may also play a role
in managing death, mutations and flaws.
The management of resources by the operating system is controlled
by algorithms. From the point of view of the digital organisms these
take the form of a set of logical rules like those embodied in the
logic of the instruction set. In this way, the operating system
is a defining part of the physics and chemistry of the digital
universe. Evolution will explore the possibilities inherent in
these rules, finding ways to more efficiently gain access to and
exploit the resources managed by the operating system.
10. Spatial Topology
Digital organisms live in the memory space of computers, predominantly
in the RAM memory, although they could also live on disks or any other
storage device, or even within networks to the extent that the networks
themselves can store information. In essence, digital organisms
live in the space that has been referred to as ``cyber-space''.
It is worthwhile reflecting on the topology of this space, as it is
a radically different space from the one we live in.
A typical UNIX workstation, or MacIntosh computer includes a RAM memory
that can contain some megabytes of data. This is ``flat'' memory,
meaning that it is essentially unstructured. Any location in memory
can be accessed through its numeric address. Thus adjacent locations
in memory are accessed through successive integer values. This addressing
convention causes us to think of the memory as a linear space, or a
one-dimensional space.
However, this apparent one-dimensionality of the RAM memory is something
of an illusion generated by the addressing scheme. A better way of
understanding the topology of the memory comes from asking ``what is the
distance between two locations in memory''. In fact the distance can not
be measured in linear units. The most appropriate unit is the time that
it takes to move information between the two points.
Information contained in the RAM memory can not move directly from
point to point. Instead the information is transferred from the RAM to
a register in the CPU, and then from the CPU back to the new location
in RAM. Thus the distance between two locations in RAM is just the time
that it takes to move from the RAM to the CPU plus the time that it takes
to move from the CPU to the RAM. Because all points in the RAM are
equidistant from the CPU, the distance between any pair of locations in
the RAM is the same, regardless of how far apart they may appear based
on their numeric addresses.
A space in which all pairs of points are equidistant is clearly not a
Euclidean space. That said, we must recognize however, that there
are a variety of ways in which memory is normally addressed, that gives
it the appearance, at least locally, of being one dimensional. When
code is executed by the CPU, the instruction pointer generally increments
sequentially through memory, for short distances, before jumping to
some other piece of code. For those sections of code where instructions
are sequential, the memory is effectively one-dimensional. In addition,
searches of memory are often sequentially organized (e.g., the search
for complementary templates in Tierra). This again makes the memory
effectively one-dimensional within the search radius. Yet even under
these circumstances, the memory is not globally one-dimensional. Rather
it consists of many small one dimensional pieces, each of which
has no meaningful spatial relationship to the others.
Because we live in a three-dimensional Euclidean space, we tend to impose
our familiar concepts of spatial topology onto the computer memory. This
leads first to the erroneous perception that memory is a one-dimensional
Euclidean space, and second, it often leads to the conclusion that the
digital world could be enriched by increasing the dimensionality of the
Euclidean memory space.
Many of the serious efforts to extend the Tierra model have included as
a central feature, the creation of a two-dimensional space for the
creatures to inhabit ( BaDa, Davi1, Davi2, Male, Skip ).
The logic behind the motivation derives from contemplation of the extent
to which the dimensionality of the space we live in permits the richness
of pattern and process that we observe in nature. Certainly if our
universe were reduced from three to two dimensions, it would eliminate
the possibility of most of the complexity that we observe. Imagine for
example, the limitations that two-dimensionality would place on the
design of neural networks (if ``wires'' could not cross). If we were
to further reduce the dimensionality of our universe to just one
dimension, it would probably completely preclude the possibility of the
existence of life.
It follows from these thoughts, that restricting digital life to a
presumably one-dimensional memory space places a tragic limitation on
the richness that might evolve. Clearly it would be liberating to
move digital organisms into a two or three-dimensional space. The flaw
in all of this logic derives from the erroneous supposition that
computer memory is a Euclidean space.
To think of memory as Euclidean is to fail to understand its natural
topology, and is an example of one of the greatest pitfalls in the
enterprise of synthetic biology: to transfer a concept from organic
life to synthetic life in a way that is ``un-natural'' for the artificial
medium. The fundamental principal of the approach I am advocating
is to respect the nature of the medium into which life is being
inoculated, and to find the natural form of life in that medium ,
without inappropriately trying to make it like organic life.
The desire to increase the richness of memory topology is commendable,
however this can be achieved without forcing the memory into an
un-natural Euclidean topology. Let us reflect a little more on the
structure of cyberspace. Thus far we have only considered the topology
of flat memory. Let us consider segmented memory such as is found with
the notorious Intel 80X86 design. With this design, you may treat any
arbitrarily chosen block of 64K bytes as flat, and all pairs of locations
within that block are equidistant. However, once the block is chosen,
all memory outside of that block is about twice as far away.
Cache memory is designed to be accessed more rapidly than RAM memory,
thus pairs of points within cache memory are closer than pairs of points
within RAM memory. The distance between a point in cache and a point in
RAM would be an intermediate distance. The access time to memory on
disks is much greater than for RAM memory, thus the distance between
points on disk is very great, and the distance between RAM and disk is
again intermediate (but still very great). CPU registers represent a small
amount of memory locations, between which data can move very rapidly,
thus these registers can be considered to be very close together.
For networked computer systems, information can move between the memories
of the computers on the net, and the distances between these memories is
again the transfer time. If the CPU, cache, RAM and disk memories of a
network of computers are all considered together, they present a very
complex memory topology. Similar considerations apply to massively
parallel computers which have memories connected in a variety of
topologies. Utilizing this complexity moves us in the direction of what
has been intended by creating Euclidean memories for digital organisms,
but does so while fully respecting the natural topology of computer
memories.
11. Ecological Context
11.1 The Living Environment
Some rain forests in the Amazon region occur on white sand soils.
In these locations, the physical environment consists of clean white
sand, air, falling water, and sunlight. Embedded within this relatively
simple physical context we find one of the most complex ecosystems
on earth, containing hundreds of thousands of species. These species
do not represent hundreds of thousands of adaptations to the physical
environment. Most of the adaptations of these species are to the
other living organism. The forest creates its own environment.
Life is an auto-catalytic process that builds on itself. Ecological
communities are complex webs of species, each living off of others, and
being lived off of by others. The system is self-constructing,
self-perpetuating, and feeds on itself. Living organisms interface with
the non-living physical environment, exchanging materials with it, such
as oxygen, carbon-dioxide, nitrogen, and various minerals. However, in
the richest ecosystems, the living components of the environment predominate
over the physical components.
With living organisms constituting the predominant features of the
environment, the evolutionary process is primarily concerned with
adaptation to the living environment. Thus ecological interactions
are an important driving force for evolution. Species evolve adaptations
to exploit other species (to eat them, to parasitize them, to climb on
them, to nest on them, to catch a ride on them, etc.) and to defend
against such exploitation where it creates a burden.
This situation creates an interesting dynamic. Evolution is
predominantly concerned with creating and maintaining adaptations
to living organisms which are themselves evolving. This generates
evolutionary races among groups of species that interact ecologically.
These races can catalyze the evolution of upwardly spiraling complexity
as each species evolves to overcome the adaptations of the others.
Imagine for example, a predator and prey, each evolving to increase its
speed and agility, in capturing prey, or in evading capture. This
coupled evolutionary race can lead to increasingly complex nervous
systems in the evolving predator and prey species.
This mutual evolutionary dynamic is related to the Red Queen
hypothesis ( VanV ), named after the Red Queen from Alice in
Wonderland. This hypothesis suggests that in the face of a
changing environment, organisms must evolve as fast as they can
in order to simply maintain their current state of adaptation.
``In order to get anywhere you must run twice as fast as that''
( Carr ).
If organisms only had to adapt to the non-living environment, the race
would not be so urgent. Species would only need to evolve as fast as the
relatively gradual changes in the geology and climate. However, given that
the species that comprise the environment are themselves evolving, the
race becomes rather hectic. The pace is set by the maximal rate that
species may change through evolution, and it becomes very difficult to
actually get ahead. A maximal rate of evolution is required just to keep
from falling behind.
What all of this discussion points to is the importance of embedding
evolving synthetic organisms into a context in which they may interact
with other evolving organisms. A counter example is the standard
implementations of genetic algorithms in which the evolving entities
interact only with the fitness function, and never ``see'' the other
entities in the population. Many interesting behavioral, ecological
and evolutionary phenomena can only emerge from interactions among
the evolving entities.
11.2 Diversity
Major temporal and spatial patterns of organic diversity on earth remain
largely unexplained, although there is no lack of theories. Diversity
theories suggest fundamental ecological and evolutionary principles which
may apply to synthetic life. In general these theories relate to
synthetic life in two ways: 1) They suggest factors which may be critical
to the auto-catalytic increase of diversity and complexity in an evolving
system. It may be necessary then to introduce these factors into an
artificial system to generate increasing diversity and complexity.
2) Because it will be possible to manipulate the presence, absence, or
state of these factors in an artificial system, the artificial system may
provide an experimental framework for examining evolutionary and
ecological processes that influence diversity.
The Gaussian principle of competitive exclusion states that no two species
that occupy the same niche can coexist. The species which is the superior
competitor will exclude the inferior competitor. The principle has been
experimentally demonstrated in the laboratory, and is considered
theoretically sound. However, natural communities widely flaunt the
principle. In tropical rain forests several hundred species of trees
coexist without any dominant species in the community. All species of
trees must spread their leaves to collect light and their roots to absorb
water and nutrients. Evidently there are not several hundred niches for
trees in the same habitat. Somehow the principle of competitive exclusion
is circumvented.
There are many theories on how competitive exclusion may be circumvented.
One leading theory is that periodic disturbance at the proper level sets
back the process of competitive exclusion, allowing more species to
coexist ( Hust79, Hust92, Hust93 ). There is substantial evidence that
moderate levels of disturbance can increase diversity. In a digital
community, disturbance might take the form of freeing blocks of memory
that had been filled with digital organisms. It would be very easy to
experiment with differing frequencies and patch sizes of disturbance.
One theory to explain the great increase in diversity and complexity in
the Cambrian explosion ( Stan ) states that its evolution was driven
by ecological interactions, and that it was originally
sparked by the appearance of the first organisms that ate other
organisms (heterotrophs). As long as all organisms were autotrophs
(produce their own food, like plants), there was only room for a few
species. In a community with only one trophic level, the most successful
competitors would dominate. The process of competitive exclusion would
keep diversity low.
However, when the first herbivore (organisms that eat autotrophs)
appeared it would have been selected to prefer the most common species
of algae, thereby preventing any species of algae from dominating.
This opens the way for more species of algae to coexist. Once the
``heterotroph barrier'' had been crossed, it would be simple for
carnivores to arise, imposing a similar diversifying effect on
herbivores. With more species of algae, herbivores may begin to
specialize on different species of algae, enhancing diversification
in herbivores. The theory states that the process was
auto-catalytic, and set off an explosion of diversity.
One of the most universal of ecological laws is the species area
relationship ( MaWi ). It has been demonstrated that in a wide variety of
contexts, the number of species occupying an ``area'' increases with the
area. The number of species increases in proportion to the area raised to
a power between 0.1 and 0.3. S=KA^z , where 0.1 < z < 0.3.
The effect is thought to result from the equilibrium species number being
determined by a balance between the arrival (by immigration or speciation)
and local extinction of species. The likelihood of extinction is greater
in small areas because they support smaller populations, for which a
fluctuation to a size of zero is more likely. If this effect holds for
digital organisms it suggests that larger amounts of memory will generate
greater diversity.
11.3 Ecological Attractors
While there are no completely independent instances of natural evolution
on Earth, there are partially independent instances. Where major
diversifications have occurred, isolated either by geography or epoch
from other similar diversifications, we have the opportunity to observe
whether evolution tends to take the same routes or is always quite
different. We can compare the marsupial mammals of Australia to the
placental mammals of the rest of the world, or the modern mammals to
the reptiles of the age of dinosaurs, or the bird fauna of the Galapagos
to the bird faunas of less isolated islands.
What we find again and again is an uncanny convergence between these
isolated faunas. This suggests that there are fairly strong ecological
attractors which evolution will tend to fill, more or less regardless
of the developmental and physiological systems that are evolving.
In this view, chance and history still play a role, in determining
what kind of organism fills the array of ecological attractors
(reptiles, mammals, birds, etc.), but the attractors themselves may
be a property of the system and not as variable. Synthetic systems
may also contain fairly well defined ecological forms which may
be filled by a wide variety of specific kinds of organisms.
Given their evident importance in moving evolution, it is important
to include ecological interactions in synthetic instantiations of
life. It is encouraging to observe that in the Tierra model, ecological
interactions, and the corresponding evolutionary races emerged
spontaneously. It is possible that any medium into which evolution
is inoculated will contain an array of ``ecological attractors'' into
which evolution will easily flow.
12. Cellularity
Cellularity is one of the fundamental properties of organic life, and can
be recognized in the fossil record as far back as 3.6 billion years. The
cell is the original individual, with the cell membrane defining its limits
and preserving its chemical integrity. An analog to the cell membrane is
probably needed in digital organisms in order to preserve the integrity of
the informational structure from being disrupted by the activity of other
organisms.
The need for this can be seen in AL models such as cellular automata where
virtual state machines pass through one another ( Lang86 ), or in core
wars type simulations where coherent structures that arise demolish one
another when they come into contact ( Rasm90,Rasm91 ). An analog to
the cell membrane that can be used in the core wars type of simulation is
memory allocation. An artificial ``cell'' could be defined by the limits
of an allocated block of memory. Free access to the memory within the
block could be limited to processes within the block. Processes outside
of the block would have limited access, according the rules of
``semi-permeability''; for example they might be allowed to read and
execute but not write.
13. Multi-cellularity
Multi-celled digital organisms are parallel processes. By attempting
to synthesize multi-celled digital organisms we can simultaneously
explore the biological issues surrounding the evolutionary transition
from single-celled to multi-celled life, and the computational issues
surrounding the design of complex parallel software.
13.1 Biological Perspective --- Cambrian Explosion
Life appeared on earth somewhere between three and four billion years
ago. While the origin of life is generally recognized as an event of
the first order, there is another event in the history of life that is
less well known but of comparable significance. The origin of biological
diversity and at the same time of complex macroscopic multi-cellular
life, occurred abruptly in the Cambrian explosion 600 million years ago.
This event involved a riotous diversification of life forms. Dozens of
phyla appeared suddenly, many existing only fleetingly, as diverse and
sometimes bizarre ways of life were explored in a relative ecological void
( Goul, Morr ).
The Cambrian explosion was a time of phenomenal and spontaneous increase
in the complexity of living systems. It was the process initiated at
this time that led to the evolution of immune systems, nervous systems,
physiological systems, developmental systems, complex morphology, and
complex ecosystems. To understand the Cambrian explosion is to understand
the evolution of complexity. If the history of organic life can be used
as a guide, the transition from single celled to multi-celled organisms
should be critical in achieving a rich diversity and complexity
of synthetic life forms.
13.2 Computational Perspective --- Parallel Processes
It has become apparent that the future of high performance computing
lies with massively parallel architectures. There already exist a
variety of parallel hardware platforms, but our ability to fully
utilize the potential of these machines is constrained by our
inability to write software of a sufficient complexity.
There are two fairly distinctive kinds of parallel architecture in
use today: SIMD (single instruction multiple data) and MIMD (multiple
instruction multiple data). In the SIMD architecture, the machine may
have thousands of processors, but in each CPU cycle, all of the processors
must execute the same instruction, although they may operate on different
data. It is relatively easy to write software for this kind of machine,
since what is essentially a normal sequential program will be broadcast to
all the processors.
In the MIMD architecture, there exists the capability for each of the
hundreds or thousands of processors to be executing different code, but
to have all of that activity coordinated on a common task. However, there
does not exist an art for writing this kind of software, at least not
on a scale involving more than a few parallel processes. In fact it
seems unlikely that human programmers will ever be capable of actually
writing software of such complexity.
13.3 Evolution as a Proven Route
It is generally recognized that evolution is the only process with
a proven ability to generate intelligence. It is less well recognized
that evolution also has a proven ability to generate parallel software
of great complexity. In making life a metaphor for computation we
will think of the genome, the DNA, as the program, and we will think
of each cell in the organism as a processor (CPU). A large multi-celled
organism like a human contains trillions of cells/processors. The
genetic program contains billions of nucleotides/instructions.
In a multi-celled organism, cells are differentiated into many cell
types such as brain cells, muscle cells, liver cells, kidney cells,
etc. The cell types just named are actually general classes of cell
types within which there are many sub-types. However, when we specify
the ultimate indivisible types, what characterizes a type is the set
of genes it expresses. Different cell types express different combinations
of genes. In a large organism, there will be a very large number of
cells of most types. All cells of the same type express the same genes.
The cells of a single cell type can be thought of as exhibiting
parallelism of the SIMD kind, as they are all running the same ``program''
by expressing the same genes. Cells of different cell types exhibit
MIMD parallelism as they run different code by expressing different
genes. Thus large multi-cellular organisms display parallelism on an
astronomical scale, combining both SIMD and MIMD parallelism into a
beautifully integrated whole. From these considerations it is evident
that evolution has a proven ability to generate massively parallel
software embedded in wetware. The computational goal of evolving
multi-cellular digital organisms is to produce such software embedded
in hardware.
13.4 Fundamental Definition
In order to conceptualize multi-cellularity in the context of an
artificial medium, we must have a very fundamental definition which
is independent of the context of the medium. We generally think
of the defining property of multi-cellularity as being that the
cells stick together, forming a physically coherent unit. However,
this is a spatial concept based on Euclidean geometry, and therefore
is not relevant to non-Euclidean cyberspace.
While physical coherence might be an adequate criteria for recognizing
multi-cellularity in organic organisms, it is not the property that
allows multi-cellular organisms to become large and complex. There are
algae that consist of strands of cells that are stuck together, with each
cell being identical to the next. This is a relatively limiting form
of multi-cellularity because there is no differentiation of cell types.
It is the specialization of functions resulting from cell differentiation
that has allowed multi-cellular organisms to attain large sizes and great
complexity. It is differentiation that has generated the MIMD style
of parallelism in organic software.
From an evolutionary perspective, an important characteristic of
multi-cellular organisms is their genetic unity. All the cells of
the individual contain the same genetic material as a result of having
a common origin from a single egg cell (some small genetic differences
may arise due to somatic mutations; in some species new individuals
arise from a bud of tissue rather than a single cell). Genetic unity
through common origin, and differentiation are critical qualities of
multi-cellularity that may be transferable to media other than organic
chemistry.
Buss ( Buss ) provides a provocative discussion of the evolution of
multi-cellularity, and explores the conflicts between selection at the
levels of cell lines and of individuals. From his discussion the
following idea emerges (although he does not explicitly state this idea,
in fact he proposes a sort of inverse of this idea, p. 65): the
transition from single to multi-celled existence involves the extension
of the control of gene regulation by the mother cell to successively
more generations of daughter cells.
In organic cells, genes are regulated by proteins contained in the
cytoplasm. During early embryonic development in animals, an initially
very large fertilized egg cell undergoes cell division with no increase
in the overall size of the embryo. The large cell is simply partitioned
into many smaller cells, and all components of the cytoplasm are of
maternal origin. By preventing several generations of daughter cells
from producing any cytoplasmic regulatory components, the mother gains
control of the course of differentiation, and thereby creates the
developmental process. In single celled organisms by contrast, after
each cell division, the daughter cell produces its own cytoplasmic
regulatory products, and determines its own destiny independent of the
mother cell.
Complex digital organisms will be self replicating algorithms, consisting
of many distinct processes dedicated to specific tasks (e.g., locating
free memory, mates or other resources; defense; replicating the code).
These processes must be coordinated and regulated, and may be divided
among several cells specialized for specific functions. If the mother
cell can influence the regulation of the processes of the daughter, so
as to force the daughter cell to specialize in function and express only
a portion of its full genetic potentiality, then the essence of
multi-cellularity will be achieved.
13.5 Computational Implementation
The discussion above suggests that the critical feature needed to allow
the evolution of multi-cellularity is for a cell to be able to influence
the expression of genes by its daughter cell. In the digital context,
this means that a cell must be able to influence what code is executed
by its daughter cell.
If we assume that in digital organisms, as in organic ones, all cells
in an individual contain the same genetic material, then the desired
regulatory mechanism can be achieved most simply by allowing the mother
cell to affect the context of the CPU of the daughter cell at the time
that the cell is ``born''. Most importantly, the mother cell needs to
be able to set the address of the instruction pointer of the daughter
cell at birth, which will determine where the daughter cell will begin
executing its code. Beyond that, additional influence can be achieved
by allowing the mother cell to place values in the registers of the
daughter's CPU.
A large digital genome may contain several sections of code that are
``closed'' in the sense that one section of code will not pass control
of execution to another. Thus if execution begins in one of these
sections of code, the other sections will never be expressed. This
type of genetic organization, coupled with the ability of the mother
cell to determine where the daughter cell begins executing, could
provide a mechanism of gene regulation suitable for causing the
differentiation of cells in a multi-cellular digital organism.
Other schemes for the regulation of code expression are also possible.
For example, digital computers commonly have three protection states
available for the memory: read, write and execute. If the code of
the genome were provided with execute protection, it would provide
a means of suppression of the execution of code in the protected
region of the genome.
13.6 Digital ``Neural Networks'' --- Natural Artificial Intelligence
One of the greatest challenges in the field of computer science is to
produce computer systems that are ``intelligent'' in some way. This
might involve for example, the creation of a system for the guidance
of a robot which is capable of moving freely in a complex environment,
seeking, recognizing and manipulating a variety of objects. It might
involve the creation of a system capable of communicating with humans
in natural spoken human language, or of translating between human
languages.
It has been observed that natural systems with these capabilities
are controlled by nervous systems consisting of large numbers of
neurons interconnected by axons and dendrites. Borrowing from nature,
a great deal of work has gone into setting up ``neural networks'' in
computers ( Dayh, HeKrPa ). In these systems, a collection of simulated
``neurons'' are created, and connected so that they can pass messages.
The learning that takes place is accomplished by adjusting the
``weights'' of the connections.
Organic neurons are essentially analog devices, thus when neural networks
are implemented on computers, they are digital emulations of analog
devices. There is a certain inefficiency involved in emulating
an analog device on a digital computer. For this reason, specialized
analog hardware has been developed for the more efficient implementation
of artificial neural nets ( Mead ).
Neural networks, as implemented in computers, either digital or analog,
are intentional mimics of organic nervous systems. They are designed
to function like natural neural networks in many details. However,
natural neural networks represent the solution found by evolution to
the problem of creating a control system based on organic chemistry.
Evolution works with the physics and chemistry of the medium in which
it is embedded.
The solution that evolution found to the problem of communication
between organic cells is chemical. Cells communicate by releasing
chemicals that bind to and activate receptor molecules on target
cells. Working within this medium, evolution created neural nets.
Inter-cellular chemical communication in neural nets is ``digital''
in the sense that chemical messages are either present or not present
(on or off). In this sense, a single chemical message carries only
a single bit of information. More detailed information can be derived
from the temporal pattern of the messages, and also the context of
the message. The context can include where on the target cell body
the message is applied (which influences its ``weight''), and what
other messages are arriving at the same time, with which the message
in question will be integrated.
It is hoped that evolving multi-cellular digital organisms will become
very complex, and will contain some kind of control system that fills
the functional role of the nervous system. While it seems likely that
the digital nervous system would consist of a network of communicating
``cells'', it seem unlikely that this would bear much resemblance to
conventional neural networks.
Compare the mechanism of inter-cellular communication in organic cells
(described above), to the mechanisms of inter-process communication in
computers. Processes transmit messages in the form of bit patterns,
which may be of any length, and so which may contain any amount of
information. Information need not be encoded into the temporal pattern
of impulse trains. This fundamental difference in communication
mechanisms between the digital and the organic mediums must influence
the course that evolution will take as it creates information processing
systems in the two mediums.
It seems highly unlikely that evolution in the digital context would
produce information processing systems that would use the same forms
and mechanisms as natural neural nets (e.g., weighted connections,
integration of incoming messages, threshold triggered all or nothing
output, thousands of connections per unit). The organic medium is a
physical/chemical medium, whereas the digital medium is a
logical/informational medium. That observation alone would suggest
that the digital medium is better suited to the construction of
information processing systems.
If this is true, then it may be possible to produce digitally based
systems that have functionality equivalent to natural neural networks,
but which have a much greater simplicity of structure and process.
Given evolution's ability to discover the possibilities inherent in a
medium, and it's complete lack of preconceptions, it would be very
interesting to observe what kind of information processing systems
evolution would construct in the digital medium. If evolution is
capable of creating network based information processing systems, it
may provide us with a new paradigm for digital ``connectionism'',
that would be more natural to the digital medium than simulations of
natural neural networks.
14. Digital Husbandry
Digital organisms evolving freely by natural selection do no ``useful''
work. Natural evolution tends to the selfish needs of perpetuating
the genes. We can not expect digital organisms evolving in this way
to perform useful work for us, such as guiding robots or interpreting
human languages. In order to generate digital organisms that
function as useful software, we must guide their evolution through
artificial selection, just as humans breed dogs, cattle and rice.
Some experiments have already been done with using artificial selection
to guide the evolution of digital organisms for the performance of
``useful'' tasks ( Adam, Surk, Tack ). I envision two approaches to
the management of digital evolution: digital husbandry, and digital
genetic engineering.
Digital husbandry is an analogy to animal husbandry. This
technique would be used for the evolution of the most advanced and
complex software, with intelligent capabilities. Correspondingly,
this technique is the most fanciful. I would begin by allowing
multi-cellular digital organisms to evolve freely by natural selection.
Using strictly natural selection, I would attempt to engineer the
system to the threshold of the computational analog of the Cambrian
explosion, and let the diversity and complexity of the digital organisms
spontaneously explode.
One of the goals of this exercise would be to allow evolution to find
the natural forms of complex parallel digital processes. Our parallel
hardware is still too new for human programmers to have found the
best way to write parallel software. And it is unlikely that human
programmers will ever be capable of writing software of the
complexity that the hardware is capable of running. Evolution
should be able to show us the way.
It is hoped that this would lead to highly complex digital organisms,
which obtain and process information, presumably predominantly about
other digital organisms. As the complexity of the evolving system
increases, the organisms will process more complex information in
more complex ways, and take more complex actions in response. These
will be information processing organisms living in an informational
environment.
It is hoped that evolution by natural selection alone would lead to
digital organisms which while doing no ``useful'' work, would
none-the-less be highly sophisticated parallel information processing
systems. Once this level of evolution has been achieved, then artificial
selection could begin to be applied, to enhance those information
processing capabilities that show promise of utility to humans.
Selection for different capabilities would lead to many different
breeds of digital organisms with different uses. Good examples of
this kind of breeding from organic evolution are the many varieties
of domestic dogs which were derived by breeding from a single species,
and the vegetables cabbage, kale, broccoli, cauliflower, and brussels
sprouts which were all produced by selective breeding from a single
species of plant.
Digital genetic engineering would normally be used in conjunction with
digital husbandry. This consists of writing a piece of application code
and inserting it into the genome of an existing digital organism.
A technique being used in organic genetic engineering today is to insert
genes for useful proteins into goats, and to cause them to be expressed in
the mammary glands. The goats then secrete large quantities of the
protein into the milk, which can be easily removed from the animal. We
can think of our complex digital organisms as general purpose animals,
like goats, into which application codes can be inserted to add new
functionalities, and then bred through artificial selection to enhance or
alter the quality of the new functions.
In addition to adding new functionalities to complex digital organisms,
digital genetic engineering could be used for achieving extremely high
degrees of optimization in relatively small but heavily used pieces of
code. In this approach, small pieces of application code could be
inserted into the genomes of simple digital organisms. Then the
allocation of CPU cycles to those organisms would be based on the
performance of the inserted code. In this way, evolution could optimize
those codes, and they could be returned to their applications. This
technique would be used for codes that are very heavily used such as
compiler constructs, or central components of the operating system.
15. Living Together
I'm glad they're not real, because if they were, I would
have to feed them and they would be all over the house.
--- Isabel Ray.
Evolution is an extremely selfish process. Each evolving species does
whatever it can to insure its own survival, with no regard for the
well-being of other genetic groups (potentially with the exception of
intelligent species). Freely evolving autonomous artificial entities
should be seen as potentially dangerous to organic life, and should
always be confined by some kind of containment facility, at least until
their real potential is well understood. At present, evolving digital
organisms exist only in virtual computers, specially designed so that
their machine codes are more robust than usual to random alterations.
Outside of these special virtual machines, digital organisms are merely
data, and no more dangerous than the data in a data base or the text
file from a word processor.
Imagine however, the problems that could arise if evolving digital
organisms were to colonize the computers connected to the major networks.
They could spread across the network like the infamous internet worm
( Worm1, Worm2, Worm3, Worm4 ). When we attempted to stop them, they
could evolve mechanisms to escape from our attacks. It might conceivably
be very difficult to eliminate them. However, this scenario is highly
unlikely, as it is probably not possible for digital organisms to evolve
on normal computer systems. While the supposition remains untested,
normal machine languages are probably too brittle to support digital
evolution.
Evolving digital organisms will probably always be confined to special
machines, either real or virtual, designed to support the evolutionary
process. This does not mean however, that they are necessarily harmless.
Evolution remains a self-interested process, and even the interests of
confined digital organisms may conflict with our own. For this reason
it is important to restrict the kinds of peripheral devices that are
available to autonomous evolving processes.
This conflict was taken to its extreme in the movie Terminator 2. In
the imagined future of the movie, computer designers had achieved a very
advanced chip design, which had allowed computers to autonomously increase
their own intelligence until they became fully conscious. Unfortunately,
these intelligent computers formed the ``sky-net'' of the United States
military. When the humans realized that the computers had become
intelligent, they decided to turn them off. The computers viewed this
as a threat, and defended themselves by using one of their peripheral
devices: nuclear weapons.
Relationships between species can however, be harmonious. We presently
share the planet with millions of freely evolving species, and they are
not threatening us with destruction. On the contrary, we threaten
them. In spite of the mindless and massive destruction of life being
caused by human activity, the general pattern in living communities is
one of a network of inter-dependencies.
More to the point, there are many species with which humans live in
close relationships, and whose evolution we manage. These are the
domesticated plants and animals that form the basis of our agriculture
(cattle, rice), and who serve us as companions (dogs, cats, house plants).
It is likely that our relationship with digital organisms will develop
along the same two lines.
There will likely be carefully bred digital organisms developed by
artificial selection and genetic engineering that perform intelligent
data processing tasks. These would subsequently be ``neutered'' so that
they can not replicate, and the eunuchs would be put to work in
environments free from genetic operators. We are also likely to see
freely evolving and/or partially bred digital ecosystems contained
in the equivalent of digital aquariums (without dangerous peripherals)
for our companionship and aesthetic enjoyment.
While this paper has focused on digital organisms, it is hoped that
the discussions be taken in the more general context of the possibilities
of any synthetic forms of life. The issues of living together become
more critical for synthetic life forms implemented in hardware or
wetware. Because these organisms would share the same physical space
that we occupy, and possibly consume some of the same material resources,
the potential for conflict is much higher than for digital organisms.
At the present, there are no self-replicating artificial organisms
implemented in either hardware or wetware (with the exception of some
simple organic molecules with evidently small and finite evolutionary
potential ( Rebe1, Rebe3, Rebe2 ). However, there are active
attempts to synthesize RNA molecules capable of replication
( Joyc2, Joyc1 ), and there is much discussion of the future
possibility of self-replicating nano-technology and macro-robots.
I would strongly urge that as any of these technologies approaches the
point where self-replication is possible, the work be moved to specialized
containment facilities. The means of containment will have to be handled
on a case-by-case basis, as each new kind of replicating technology will
have its own special properties.
There are many in the artificial life movement who envision a beautiful
future in which artificial life replaces organic life, and expands out
into the universe ( Levy1, Levy2, Mora1, Mora2, Mora3 ). The motives
vary from a desire for immortality to a vision of converting virtually
all matter in the universe to living matter. It is argued that this
transition from organic to metallic based life is the inevitable and
natural next step in evolution.
The naturalness of this step is argued by analogy with the supposed
genetic takeovers in which nucleic acids became the genetic material
taking over from clays ( CaSm ), and cultural evolution took over
from DNA based genetic evolution in modern humans. I would point out
that whatever nucleic acids took over from, it marked the origin of
life more than the passing of a torch. As for the supposed transition
from genetic to cultural evolution, the truth is that genetic evolution
remains intact, and has had cultural evolution layered over it rather
than being replaced by it.
The supposed replacement of genetic by cultural evolution remains a
vision of a brave new world, which has yet to materialize. Given
the ever increasing destruction of nature, and human misery and violence
being generated by human culture, I would hesitate to place my trust
in the process as the creator of a bright future. I still trust in
organic evolution, which created the beauty of the rainforest through
billions of years of evolution. I prefer to see artificial evolution
confined to the realm of cyberspace, where we can more easily coexist
with it without danger, using it to enhance our lives without having to
replace ourselves.
As for the expansion of life out into the universe, I am confident that
this can be achieved by organic life aided by intelligent non-replicating
machines. And as for immortality, our unwillingness to accept our own
mortality has been a primary fuel for religions through the ages. I
find it sad that Artificial Life should become an outlet for the same
sentiment. I prefer to achieve immortality in the old fashioned organic
evolutionary way, through my children. I hope to die in my patch of
Costa Rican rain forest, surrounded by many thousands of wet and squishy
species, and leave it all to my daughter. Let them set my body out in
the jungle to be recycled into the ecosystem by the scavengers and
decomposers. I will live on through the rain forest I preserved, the
ongoing life in the ecosystem into which my material self is recycled,
the memes spawned by my scientific works, and the genes in the daughter
that my wife and I created.
16. Challenges
For well over a century, evolution has remained a largely
theoretical science. Now new technologies have allowed us
to inoculate natural evolution into artificial media, converting
evolution into an experimental and applied science, and at the
same time, opening Pandora's box. This creates a variety of
challenges which have been raised or alluded to in the preceding
essay, and which will be summarized here.
16.1 Respecting the Medium
If the objective is to instantiate rather than simulate life, then
care must be taken in transferring ideas from natural to artificial
life forms. Preconceptions derived from experience with natural life
may be inappropriate in the context of the artificial medium. Getting
it right is an art, which likely will take some skill and practice to
develop.
However, respecting the medium is only one approach, which I happen to
favor. I do not wish to imply that it is the only valid approach. It
is too early to know which approach will generate the best results,
and I hope that other approaches will be developed as well. I have
attempted to articulate clearly this ``natural'' approach to synthetic
life, so that those who choose to follow it may achieve greater
consistency in design through a deeper understanding of the method.
16.2 Understanding Evolvability
Attempts are now underway to inoculate evolution into many artificial
systems, with mixed results. Some genetic languages evolve readily,
while others do not. We do not yet know why, and this is a fundamental
and critically important issue. What are the elements of evolvability?
Efforts are needed to directly address this issue. One approach that
would likely be rewarding would be to systematically identify features
of a class of languages (such as machine languages), and one by one,
vary each feature, to determine how evolvability is affected by the
state of each feature.
16.3 Creating Organized Sexuality
Organized sexuality is important to the evolutionary process. It is
the basis of the species concept, and while remaining something of
an enigma in evolutionary theory, clearly is an important facilitator
of the evolutionary process. Yet this kind of sexuality still has not
been implemented in a natural way in synthetic life systems. It is
important to find ways of orchestrating organized sexuality in synthetic
systems such as digital organisms, in a way in which it is not mandatory,
and in which the organisms must carry out the process through their
own actions.
16.4 Creating Multi-cellularity
In organic life, the transition from single to multi-celled forms
unleashed a phenomenal explosion of diversity and complexity. It would
seem then that the transition to multi-cellular forms could generate
analogous diversity and complexity in synthetic systems. In the case
of digital organisms, it would also lead to the evolution of parallel
processes, which could provide us with new paradigms for the design of
parallel software. The creation of multi-celled digital organisms
remains an important challenge.
16.5 Controlling Evolution
Humans have been controlling the evolution of other species for tens
of thousands of years. This has formed the basis of agriculture, through
the domestication of plants and animals. The fields of genetic
algorithms ( Gold, Holl ), and genetic programming ( Koza ) are
based on controlling the evolution of computer programs. However, we
still have very little experience with controlling the evolution of
self-replicating computer programs, which is more difficult. In addition,
breeding complex parallel programs is likely to bring new challenges.
Developing technologies for managing the evolution of complex software
will be critical for harnessing the full potential of evolution for
the creation of useful software.
16.6 Living Together
If we succeed in harnessing the power of evolution to create complex
synthetic organisms capable of sophisticated information processing
and behavior, we will be faced with the problems of how to live
harmoniously with them. Given evolution's selfish nature and
capability to improve performance, there exists the potential for
a conflict arising through a struggle for dominance between organic
and synthetic organisms. It will be a challenge to even agree on
what the most desirable outcome should be, and harder still to
accomplish it. In the end the outcome is likely to emerge from the
bottom up through the interactions of the players, rather than being
decided through rational deliberations.
Acknowledgements
This work was supported by grants CCR-9204339 and BIR-9300800
from the United States National Science Foundation, a grant from the
Digital Equipment Corporation, and by the Santa Fe Institute, Thinking
Machines Corp., IBM, and Hughes Aircraft.
This work was conducted while at:
School of Life Health Sciences, University of Delaware, Newark,
Delaware, 19716, USA, ray@udel.edu;
and Santa Fe Institute, 1660 Old Pecos Trail, Suite A, Santa Fe,
New Mexico, 87501, USA, ray@santafe.edu.
Bibliography
Adam
Adami, Chris. Unpublished. Learning and complexity in genetic
auto-adaptive systems. Caltech preprint: MAP -- 164, One of the
Marmal Aid Preprint Series In Theoretical Nuclear Physics,
October 1993. Adami has used the input-output facilities of the new
Tierra languages to feed data to creatures, and select for responses
that result from simple computations, not contained in the seed genome.
Contact: chris@almach.caltech.edu
Worm1
Anonymous. 1988. Worm invasion. Science 11-11-88: 885.
BaDa
Barton-Davis, Paul. Unpublished. Independent implementation
of the Tierra system, contact: pauld@cs.washington.edu.
Joyc2
Beaudry, Amber A., and Gerald F. Joyce. 1992. Directed evolution of
an RNA enzyme. Science 257: 635--641.
Bell
Bell, Graham. 1982. The masterpiece of nature: the evolution and genetics
of sexuality. Berkeley: University of California Press.
Benn
Benner, Steven A., Andrew D. Ellington, and Andreas Tauer. 1989.
Modern metabolism as a palimpsest of the RNA world. Proc. Natl. Acad. Sci.
U.S.A. 86: 7054--7058.
Broo
Brooks, Rodney. Unpublished. Brooks has created his own Tierra-like
system, which he calls Sierra. In his implementation, each machine
instruction consists of an opcode and an operand. Successive instructions
overlap, such that the operand of one instruction is interpreted as the
opcode of the next instruction. Contact: brooks@ai.mit.edu
Worm2
Burstyn, Harold L. 1990. RTM and the worm that ate internet. Harvard
Magazine 92(5): 23--28.
Buss
Buss, Leo W. 1987. The evolution of individuality. Princeton, NJ:
Princeton University Press. Pp. 203.
CaSm
Cairn-Smith, A. G. 1985. Seven clues to the origin of life.
Cambridge: Cambridge University Press.
Carr
Carroll, L. 1865. Through the Looking-Glass. London: MacMillan.
Chao
Chao, Lin, Christopher Vargas, Brian B. Spear, and Edward C. Cox. 1983.
Transposable elements as mutator genes in evolution. Nature 303: 633--635.
Char
Charlesworth, B. 1976. Recombination modification in a fluctuating
environment, Genetics 83: 181--195.
Darw59
Darwin, Charles. 1859. On the origin of species by means of natural
selection or the preservation of favored races in the struggle for life.
London: Murray.
Davi1
Davidge, Robert. 1992. Processors as organisms. CSRP 250. School of
Cognitive and Computing Sciences, University of Sussex. Presented at
the ALife III conference. Contact: robertd@cogs.susx.ac.uk
Davi2
Davidge, Robert. 1993. Looping as a means to survival: playing Russian
roulette in a harsh environment. In : Self organization and life:
from simple rules to global complexity, proceedings of the second
European conference on artificial life. Contact: robertd@cogs.susx.ac.uk
DaBr
Davidson, Eric H., and Roy J. Britten. 1979. Regulation of gene expression:
Possible role of repetitive sequences. Science 204: 1052--1059.
Dayh
Dayhoff, Judith. 1990. Neural network architectures. Van Nostrand
Reinhold. Pp. 259.
DeAn
DeAngelis, D., and L. Gross [eds]. 1992. Individual based models
and approaches in ecology. New York: Chapman and Hill.
DeGr
de Groot, Marc. Unpublished. Primordial soup, a Tierra-like system that has
the additional ability to spawn self-reproducing organisms from a sterile
soup. Contact: marc@kg6kf.ampr.org, marc@toad.com, marc@remarque.berkeley.edu
DoSa
Doolittle, W. Ford, and Carmen Sapienza. 1980. Selfish genes, the phenotype
paradigm and genome evolution. Nature 284: 601--603.
Eige
Eigen, Manfred. 1993. Viral quasispecies. Scientific American 269(1):
32--39.
FaBe
Farmer, J. D. Belin, A. Artificial life: the coming evolution.
Proceedings in celebration of Murray Gell-Man's 60th Birthday.
Cambridge: University Press. (Reprinted in Artificial Life II.
Pp. 815--840.)
Fefe
Feferman, Linda. 1992. Simple rules... complex behavior [video].
Santa Fe, NM: Santa Fe Institute. Contact: fef@santafe.edu,
0005851689@mcimail.com
Rebe1
Feng, Q., Park, T. K. Rebek, J. 1992. Science 254: 1179--1180.
Ghis
Ghiselin, Michael. 1974. The economy of nature and the evolution
of sex. Berkeley: University of California Press.
Gold
Goldberg, D. E. 1989. Genetic algorithms in search, optimization,
and machine learning. Reading, MA: Addison-Wesley.
Goul
Gould, Steven J. 1989. Wonderful life. W. W. Norton Company, Inc.
Pp. 347.
Gray
Gray, James. Unpublished. Natural selection of computer programs.
This may have been the first Tierra-like system, but evolving real
programs on a real rather than a virtual machine, and predating Tierra
itself: ``I have attempted to develop ways to get computer programs to
function like biological systems subject to natural selection.... I don't
think my systems are models in the usual sense. The programs have
really competed for resources, reproduced, run, and `died'. The
resources consisted primarily of access to the CPU and partition
space.... On a PDP11 I could have a population of programs running
simultaneously.'' Contact: Gray.James_L+@northport.va.gov
Gree
Green, Melvin M. 1988. Mobile DNA elements and spontaneous gene mutation.
In M. E. Lambert, J. F. McDonald, I. B. Weinstein [eds.]:
Eukaryotic transposable elements as mutagenic agents. Pp. 41--50.
Banbury Report 30, Cold Spring Harbor Laboratory.
Halv
Halvorson, Herlyn O., and Albert Monroy. 1985. The origin and evolution
of sex. New York: A. R. Liss.
Hapg
Hapgood, Fred. 1979. Why males exist: an inquiry into the evolution
of sex. New York: William Morrow.
HeKrPa
Hertz, John, Anders Krogh, and Richard G. Palmer. 1991. Introduction
to the theory of neural computation. Addison-Wesley Publishing Co.
Pp. 327.
Hoge
Hogeweg, P. 1989. Mirror beyond mirror: puddles of life.
In Langton, C. [ed], Artificial Life, Santa Fe Institute
Studies in the Sciences of Complexity, vol. VI, 297--316.
Redwood City, CA: Addison-Wesley.
Holl
Holland, John Henry. 1975. Adaptation in natural and artificial systems:
an introductory analysis with applications to biology, control, and
artificial intelligence (Univ. of Michigan Press, Ann Arbor).
Rebe3
Hong, J. I., Feng, Q., Rotello, V. Rebek, J. 1992.
Science 255: 848--850.
Hust79
Huston, Michael. 1979. A general hypothesis of species diversity,
Am. Nat. 113: 81--101.
Hust92
Huston, Michael. 1992. Biological diversity and human resources,
Impact of science on society 166: 121--130.
Hust93
Huston, Michael. 1993. Biological diversity: the coexistence of
species on changing landscapes. Cambridge University Press.
Hust88
Huston, M., DeAngelis, D., and Post, W. 1988. New computer models
unify ecological theory. Bioscience 38(10): 682--691.
JeSc
Jelinek, Warren R., and Carl W. Schmid. 1982. Repetitive sequences in
eukaryotic DNA and their expression. Ann. Rev. Biochem. 51: 813--844.
Joyc1
Joyce, Gerald F. 1992. Directed molecular evolution. Scientific
American, December 1992: 90--97.
Kamp1
Kampis, George. 1993. Coevolution in the computer: the necessity and
use of distributed code systems. Printed in the ECAL93 proceedings,
Brussels. Contact: gk@cfnext.physchem.chemie.uni-tuebingen.de
Kamp2
Kampis, George. 1993. Life-like computing beyond the machine metaphor.
In : R. Paton [ed]: Computing with biological metaphors, London:
Chapman and Hall. Contact: gk@cfnext.physchem.chemie.uni-tuebingen.de
Kauf
Kauffman, Stuart A. 1993. The origins of order, self-organization and
selection in evolution. Oxford University Press. Pp. 709.
Kere
Kerem, Bat-sheva, Johanna M. Rommens, Janet A. Buchanan, Danuta Markiewicz,
Tara K. Cox, Aravinda Chakravarti, Manuel Buchwald, and Lap-Chee Tsui.
1989. Identification of the cystic fibrosis gene: genetic analysis.
Science 245: 1073--1080.
Koza
Koza, John R. 1992. Genetic programming, on the programming of
computers by means of natural selection. Cambridge, MA: MIT Press.
Lang86
Langton, C. G. 1986. Studying artificial life with cellular automata.
Physica 22D: 120--149.
Levy1
Levy, Steven. 1992. Artificial Life, the quest for a new creation.
Pantheon Books, New York. Pp. 390.
Levy2
Levy, Steven. 1992. A-Life Nightmare. Whole Earth Review
Fall 1992, p. 22.
Lith
Litherland, J. 1993. Open-ended evolution in a computerised ecosystem.
A Masters of Science dissertation in the Department of Computer Science,
Brunel University. Contact: david.martland@brunel.ac.uk
MaWi
Mac Arthur, Robert H., and Edward O. Wilson. 1967. The theory of
island biogeography. Princeton University Press. Pp. 203.
Male
Maley, Carlo C. 1993. A model of early evolution in two dimensions.
Masters of Science thesis, Zoology, New College, Oxford University.
Contact: cmaley@oxford.ac.uk
Mano
Manousek, Wolfgang. 1992. Spontane Komplexitaetsentstehung --- TIERRA,
ein Simulator fuer biologische Evolotion. Diplomarbeit, Universitaet
Bonn, Germany, Oktober 1992. Contact: Kurt Stueber,
stueber@vax.mpiz-koeln.mpg.d400.de
Marg
Margulis, Lynn, and Dorion Sagan. 1986. Origin of sex.
New Haven: Yale University Press.
Marx
Marx, Jean L. 1989. The cystic fibrosis gene is found. Science 245:
923--925.
MaSm71
Maynard Smith, J. 1971. What use is sex?, J. Theoret. Biol. 30:
319--335.
MaSm92
Maynard Smith, J. 1992. Byte-sized evolution. Nature 355: 772--773.
Mayn
Maynard Smith, J, Christopher G. Dowson, and Brian G. Spratt. 1991.
Localized sex in bacteria. Nature 349: 29--31.
Mead
Mead, Carver. 1993. Analog VLSI and neural systems. Addison-Wesley
Publishing Co. Pp. 371.
Mich
Michod, Richard E., and Bruce R. Levin. 1988. The evolution of sex:
an examination of current ideas. Sunderland, MA: Sinauer Associates.
Mora1
Moravec, Hans. 1988. Mind Children: the future of robot and human
intelligence. Cambridge, MA: Harvard University Press.
Mora2
Moravec, Hans. 1989. Human culture: a genetic takeover underway.
In Langton, C. [ed], Artificial Life, Santa Fe Institute
Studies in the Sciences of Complexity, vol. VI, 167--199.
Redwood City, CA: Addison-Wesley.
Mora3
Moravec, Hans. 1993. Pigs in cyberspace.
Extropy Winter/Spring, 1993.
Morr
Morris, S. Conway. 1989. Burgess shale faunas and the Cambrian explosion.
Science 246: 339--346.
Rebe2
Nowick, J., Feng, Q., Tijivikua, T., Ballester, P. Rebek, J. 1991.
J. Am. Chem. Soc. 113: 8831--8839.
OrCr
Orgel, L. E., and F. H. C. Crick. 1980. Selfish DNA: the ultimate parasite.
Nature 284: 604--607.
Rasm90
Rasmussen, Steen, Carsten Knudsen, Rasmus Feldberg, and Morten Hindsholm.
1990. The coreworld: emergence and evolution of cooperative structures
in a computational chemistry. Physica D 42: 111--134.
Rasm91
Rasmussen, S., C. Knudsen, and R. Feldberg. 1991. Dynamics of programmable
matter. In Langton, C., C. Taylor, J. D. Farmer, S. Rasmussen [eds],
Artificial Life II, Santa Fe Institute Studies in the Sciences of Complexity,
vol. X, 211--254. Redwood City, CA: Addison-Wesley.
Ray79
Ray, T. S. 1979. Slow-motion world of plant `behavior' visible
in rainforest. Smithsonian 9(12): 121--30.
Ray91a
Ray, T. S. 1991. An approach to the synthesis of life.
In Langton, C., C. Taylor, J. D. Farmer, S. Rasmussen [eds],
Artificial Life II, Santa Fe Institute Studies in the Sciences of
Complexity, vol. X, 371--408. Redwood City, CA: Addison-Wesley.
Ray91b
Ray, T. S. 1991. Population dynamics of digital organisms.
In Langton, C. G. [ed.], Artificial Life II Video Proceedings.
Redwood City, CA: Addison Wesley.
Ray91c
Ray, T. S. 1991. Is it alive, or is it GA?
In Belew, R. K., and L. B. Booker [eds.], Proceedings of the 1991
International Conference on Genetic Algorithms, 527--534. San Mateo, CA:
Morgan Kaufmann.
Ray91d
Ray, T. S. 1991. Evolution and optimization of digital organisms.
In Billingsley K. R., E. Derohanes, H. Brown, III [eds.],
Scientific Excellence in Supercomputing: The IBM 1990 Contest Prize Papers,
Athens, GA, 30602: The Baldwin Press, The University of Georgia.
Ray92
Ray, T. S. 1992. Foraging behaviour in tropical herbaceous
climbers (Araceae). Journal of Ecology. 80: 189--203.
RayDo
Ray, T. S. 1992. Tierra.doc. Documentation for the
Tierra Simulator V4.0, 9--9--92. Newark, DE: Virtual Life.
The full source code and documentation for the Tierra program is
available by anonymous ftp at: tierra.slhs.udel.edu [128.175.41.34] and
life.slhs.udel.edu [128.175.41.33], or by contacting the author.
RayIp
Ray, T. S. In press. Evolution and complexity.
In : Cowan, George A., David Pines and David Metzger [eds.],
Complexity: Metaphor and Reality. Addison-Wesley Publishing Co.
RaySu
Ray, T. S. Submitted. Evolution, complexity, entropy,
and artificial reality. Physica D.
Romm
Rommens, Johanna M., Michael C. Iannuzzi, Bat-sheva Kerem, Mitchell L. Drumm,
Georg Melmer, Michhael Dean, Richard Rozmahel, Jeffery L. Cole, Dara Kennedy,
Noriko Hidaka, Martha Zsiga, Manuel Buchwald, John R. Riordan, Lap-Chee Tsui,
Francis S. Collins. 1989. Identification of the cystic fibrosis gene:
chromosome walking and jumping. Science 245: 1059--1065.
Rior
Riordan, John R., Johanna M. Rommens, Bat-sheva Kerem, Noa Alon, Richard
Rozmahel, Zbyszko Grzelczak, Julian Zielenski, Si Lok, Natasa Plavsic,
Jia-Ling Chou, Mitchell L. Drumm, Michael C. Lannuzzi, Francis S Collins,
Lap-Chee Tsui. 1989. Identification of the cystic fibrosis gene: cloning
and characterization of complementary DNA. Science 245: 1066--1073.
Skip
Skipper, Jakob. 1992. The computer zoo -- evolution in a box. In :
Francisco J. Varela and Paul Bourgine [eds.], Toward a practice of
autonomous systems, proceedings of the first European conference on
Artificial Life. MIT Press, Cambridge, MA. Pp. 355--364.
Contact: Jakob.Skipper@copenhagen.ncr.com
Sobe
Sober, E. 1984. The nature of selection. MIT Press, Cambridge, MA.
Worm3
Spafford, Eugene H. 1989. The internet worm program: an analysis.
Computer Communication Review 19(1): 17--57. Also issued as Purdue
CS technical report TR-CSD-823. Contact: spaf@purdue.edu
Worm4
Spafford, Eugene H. 1989. The internet worm: crisis and aftermath.
CACM 32(6): 678--687. Contact: spaf@purdue.edu
Stan
Stanley, Steven M. 1973. An ecological theory for the sudden origin
of multicellular life in the late precambrian, Proc. Nat. Acad. Sci. 70:
1486--1489.
Stea
Stearns, Steven C. 1987. The evolution of sex and its consequences.
Boston: Birkh a user Verlag.
Stri
Strickberger, Monroe W. 1985. Genetics. Macmillan Publishing Co. New
York.
StRa
Strong, D. R. and T. S. Ray. 1975. Host tree location behavior of a tropical
vine ( Monstera gigantea ) by skototropism. Science, 190: 804--06.
Surk
Surkan, Al. Unpublished. Self-balancing of dynamic population sectors
that consume energy. Department of computer science, UNL. ``Tierra-like
systems are being explored for their potential applications in solving
the problem of predicting the dynamics of consumption of a single energy
carrying natural resource''. Contact: surkan@cse.unl.edu
Syva
Syvanen, Michael. 1984. The evolutionary implications of mobile
genetic elements. Ann. Rev. Genet. 18: 271--293.
Tack
Tackett, Walter, and Jean-Luc Gaudiot. 1993. Adaptation of self-replicating
digital organisms. Proceedings of the International Joint Conference on
Neural Networks, Nov. 1993, Beijing, China. IEEE Press.
Contact: tackett@ipld01.hac.com, tackett@priam.usc.edu
Tayl
Taylor, Charles E., David R. Jefferson, Scott R. Turner, and
Seth R. Goldman. 1989. RAM: artificial life for the exploration
of complex biological systems. In Langton, C. [ed], Artificial Life,
Santa Fe Institute Studies in the Sciences of Complexity, vol. VI,
275--295. Redwood City, CA: Addison-Wesley.
Thom
Thomas, C. A. 1971. The genetic organization of chromosomes.
Ann. Rev. Genet. 5: 237--256.
Todd
Todd, Peter M. 1993. Artificial death. Proceedings of the Second European
Conference on Artificial Life (ECAL93), Vol. 2, Pp. 1048--1059.
Brussels, Belgium: Universite Libre de Bruxelles.
Contact: ptodd@spo.rowland.org
VanV
Van Valen, L. 1973. A new evolutionary law. Evolutionary Theory 1: 1--30.
Will
Williams, George C. 1975. Sex and evolution. Princeton: Princeton
University Press.