An Evolutionary Approach to Synthetic Biology
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    #found this on textfiles.com, and like the points made, thought id share it#
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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.


    link to file: http://www.textfiles.com/programming/zen_life.txt
    http://www.attrition.org/gallery/computing/forum/tn/youarenot.gif.html

  2. #2
    Senior Member
    Join Date
    Jun 2002
    Posts
    394
    holy crap! thats some read.
    makes you wonder, what if we are the biological machines, created for the purpose of storing and spreading ideas - thoughts, skills, opinions.
    so what happens to us when we build a synthetic (better?) enviroment for these ideas to live on...........im scared, someone hold me..........

    i think that game black and white mimics a brains synaptic pathways, for teaching your creature things like differences between good and bad, on a far less grand scale of course, but nonetheless is doing it.
    Hmm...theres something a little peculiar here. Oh i see what it is! the sentence is talking about itself! do you see that? what do you mean? sentences can\'t talk! No, but they REFER to things, and this one refers directly-unambigeously-unmistakably-to the very sentence which it is!

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