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The Use of Computer Simulation in Studying Biological Evolution: Pure Possible Processes, Selective Regimes and Open-ended Evolution


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Title: The Use of Computer Simulation in Studying Biological Evolution: Pure Possible Processes, Selective Regimes and Open-ended Evolution

The Use of Computer Simulation in Studying
Biological Evolution Pure PossibleProcesses,
Selective Regimes and Open-ended Evolution
  • Philippe HunemanIHPST (CNRS / Université PARIS I

  • The interplay between evolutionary biology and
    computer science
  • Bioinformatics, Biocomputation
  • Genetic algorithms (lexicon  crossing over ,
    genes etc)
  • The radical AL claim digital organisms are
    themselves living entities (rather than their
    simulations) (Adami 2002). Idea of
    non-carbon-based forms of life as the essence of
  • Version  2 Darwinian evolution is an
     algorithm  (Dennett, see Maynard Smith 2000)

  • What are simulations doing / teaching ?
  • What is the role of natural selection in them ?
  • Investigates the relations between biological
    evolution and computer simulations of evolving
    entities through natural selection.

I. Typology of computer simulations in
evolutionary theory
  • A. Kinds of role of selection a1. formal
    selection context
  • Todd and Miller (1995) on sexual selection
  • (sexual selection is more an incentive for
    exploration than natural selection, since
    females, through mate choice, internalize the
    constraints of natural selection. )
  • Maley on biodiversity
  • (emergence of new species is more conditioned by
    geographical barriers than by adaptive potential)

Mikel Maron 2004) Moths and industrial
  • A2. No selection context
  • Boids (Reynolds)
  • Chu and Adami (2000) simulation of phylogenies
    whose parameter is the mean number of same-order
    daughter families of a family
  • Mc Shea (1996, 2005) increase of complexity
    with no selection

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B.Use weak and strong
  • B1. Weak model is used to test one hypothesis
    on one process it simulates the behavior of the
    entities (boids Maleys biodiversity etc.). Way
    to test hypotheses about the world

  • B2 Strong entities of the model dont
    correspond to real entities the simulation is
    meant to explore the kinds of behaviors of
    digital entities themselves (Rays Tierra,
    Hollands Echo etc.). Hypotheses are made about
    the model itself
  • digital organisms as defined by the sequence
    of instructions that constitute their genome, are
    not simulated they are physically present in the
    computer and live there (Adami, 2002)

  • Echo unrealistic assumptions concerning
    reproduction, absence of isolative reproduction
    for species, makes Echo a poor model of
    evolutionary biology (Crubelier 1997)

  • Langton-Sayama Loop

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II. Natural selection and pure possible processes
  • In the (weak or strong) simulations causal
    processes (i.e. counterfactual dependencies
    between classes of sets of cells, and global
    state at next step).

  • a1 a 2 .. a1P a2P ak ..
  • A1 n, j A2 n, j Ak n, j ..
  • b1n1 b2n1 bkn1..
  • Property P n at Step n (Add (on i) Disj (on j)
    Ai n, j )
  • Property Pn1 at Step n1  (all the bin1 )
  • If P n had not been the case, Pn1 would not
    have been the case.
  • Causation as counterfactual dependence between
    steps in Cas
  • Huneman Minds and machines 2008

  • -gt In  formal selection  contexts simulations
    those causal processes are actual  selective

  • Yet the entities in the simulations can not
    exactly match biological entities
  • In Echo, you dont have species easily, in Tierra
    no lineages etc.
  • If one system is designed to study some level of
    biological reality, the other levels are not ipso
    facto given (whereas if you have, e.g.,
    organisms you have genes and species etc.)

  • -gt In actual biology all levels of the
    hierarchies are acting together
  • So computer simulations display  pure possible
    processes  concerning the entities modelised,
    located in a target-level of the hierarchy
  • (no implicit entangling between levels)

  • In the case of formal selection simulations,
     pure  selective processes occur
  • Ex. of  natural selection  sensu Channon Echo
    or Hilliss coevolution between sorting problems
     natural selection  simulations. Yet in Echo,
    for ex., the class of possible actions is limited

III. The validation problem for computer
What do tell us such simulations ?
  • They correlate pure possible processes with
    patterns of evolution
  • They can not prove that some process caused some
    evolutionary result, but they provide candidate
    causal explanations   if pattern X is met,
    then process x is likely to have produced it
  • And other causal processes may have been at work
    but they were not so significant regarding such
    outcome (noise ???)
  • Even if we have no idea of the ecological
    context, hence of the actual selective pressures

  • Adami, Pennock, Ofria and Lenski (2003) show that
    evolution is likely to have favoured complexity
    their point is that, if there is complexity
    increasing in their sense, then deleterious
    mutations might have been selected then a
    decrease in fitness might have been involved in
    the stabilisation of more functionally complex

  • Chu and Adami (2000) investigation of the
    patterns of abundance of taxa if the
    distribution of taxa resemble a certain power-law
    scheme X, it is likely that the parameter m (mean
    number of same order daughter families of a
    family) has been in nature close to the value of
    m involved in X (i.e. m1).

The validation problem
  • Epstein (1999) the case of Anasazi settlements

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  • That does not prove that the rules ascribed to
    individuals are the accurate ones
  • see also Reynold flocking boids it excludes a
    centered-controlled social organisation (but we
    need other assumptions to make this plausible)

  • Even more the case of Arakawas simulations in
  • Analysis by Kuippers Lehnard 2001, Lehnard 2007
    drop  realism  in order to achieve efficiency

  • How are simulations to be validated in biology ?
  • Mc Shea on complexity.
  • Challenges Bonner (1988) explanation of the
    increase of complexity through selected incerase
    of size in various lineages
  • Mc Shea (2005) suggests that complexity increase
    can be produced with no natural selection, only
    variation (complexity defined by diversity)
  • models also produce patterns of complexity
    increase with patterns produced under various
    constraints (driven trend vs passive trends, with
    no selection).
  • The pattern found in the fossil records may be
    produced by such process but we need to have an
    idea about the processes likely to have actually

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  • A minimal characterisation of computer
    simulations in evolutionary biology they
    provide candidate explanations (pure possible
    processes) and null hypotheses for evolutionary
  • For the same reason (they dont accept impure
    processes which are the ones really occurring)
    they cant prove anything by themselves

  • An example worth to investigate Hubbells
    ecological neutral theory (2001)
  • It skips the level of individual selection
    generate the same outcome as what we see about
    succession, stability and persistance in

IV. application discontinuities in evolution
  • 4.1. The longstanding problem with innovations
  • Darwinism is gradualist (small mutations selected
  • Cumulative selection accounts for adaptations

  • Novelties,
  • Innovations (qualitative, eg morphological,
  • Key innovations trigger adaptive radiation, and
    new phylogenetic patterns (avian wing, fish
    gills, language) id est, phylogenetic and
    ecological causal patterns

  • Pattern and processes the role of  punctuated
    equilibria theory  (Eldredge and Gould 1976)

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An issue with discontinuity
  • Problem the fitness value of half a novelty ?
    (half a wing !)
  • -gt Solutions
  • Find a benefit for each stage in various species
    (Darwin on the eye)
  • Conceive of it as an exaptation (ex. feathers)
    (Gould and Vrba 1981)
  • Developmental processes (Gould, 1977 Muller and
    Newman, 2005, etc.) variation is not  minor ,
    its a rearrangement of structures through
    shuffling of developmental modules/time (as such
    the pucntuated quilibria pattern dont require a
    specific process)

4.2. Exploring discontinuity
  • Compositional evolution (Watson 2005)
    evolutionary processes involving the combination
    of systems and susbsystems of semi-independently
    preadapted genetic material (p.3).
  • consideration of building blocks obeying some new
    rules that are inspired by the biological
    phenomena of sex and of symbiosis proves that in
    those processes non gradual emergence of
    novelties is possible.

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  • 1. A system with weak interdepencies between
    parts can undergo linear evolution increases in
    complexity are linear functions of the values of
    the variables describing the system. Algorithms
    looking for optimal solutions in this way are
    called hill-climbers they are paradigmatically
    gradual. They easily evolve systems more complex
    in a quantitative way, but they cant reach
    systems that would display innovations..

  • 2. If you have arbitrary strong complexities
    between the parts, then evolving a new complex
    system will take exponential time (time increases
    as an exponential function of the number of
    variables). Here, the best algorithm to find
    optimal solutions is the random search.
  • 3. But if you have modular interdependencies
    (encapsulated parts, etc.) between parts, then
    evolving new complex systems is a polynomial
    function of the variables. (Watson 2005,

  • Algorithms of the class divide-and conquer are
    dividing in subparts the optimisation issue, and
    divide in its turn each subpart in other subparts
    the initial exponential complexity of the
    optimisation problem approached through random
    search is thereby divided each time that the
    general system is divided so that in the end
    the problem has polynomial complexity.
  • Those algorithms illustrate how to evolve systems
    that are not gradual or linear improvements of
    extant systems but as polynomial functions of
    the variables, they are feasible in finite time,
    unlike random search processes.

  • Compositional evolution concerns pure processes
    that embody those classes of algorithms with
    polynomial rates of complexification, and have
    genuine biological correspondents sex
    symbiosis. mechanisms that encapsulate a group
    of simple entities into a complex entity (Watson
    2005, 3), and thus proceed exactly in the way
    algorithmically proper to polynomial-time
    complexity-increasing algorithms like divide and

  • Watson refined the usual crossover clause in GA,
    integrating various algorithmic devices (for ex.
    messy GA, according to Goldberg, Korb and Deb
    1989) in order to account for selection on blocks
    that take into account correlation between
    distant blocks, hence creation of new blocks
    (Watson 2005, 77).

  • . This proves that processes formally structured
    like those encapsulated processes such are
    symbiosis, endosymbiosis, may be lateral gene
    transfer have been likely to provide
    evolvability towards the most complex
    innovations, the ones not reachable through
    gradual evolution

  • The bulk of the demonstration is the identity
    between algorithmic classes (hill-climbing,
    divide-and-conquer, random search) and
    evolutionary processes (gradual evolution,
    compositional evolution).
  • So the solution of the gradualism issue is
    neither a quest of non-darwinian explanation
    (order for free, etc.), nor a reassertion of
    the power of cumulative selection that needs to
    be more deeply investigated (Mayr ), but the
    formal designing of new modes of selective
    processes, of the reason of their differences,
    and of the differences between their evolutionary
    potentials. In this sense, discontinuities in
    evolution appear as the explananda of a variety
    of selective processes whose proper features and
    typical evolutionary patterns are demonstrated by
    computer sciences

Open-ended evolution
  • Potential for discontinuities and novelties is
    constant or increasing
  • New adaptive radiations wings for insects and
    birds, etc. as opening possibilities of for
    other novelties
  • Not predictible but retrodictible

Modelling open-ended evolution
  • Question what is specific to evolution in the
    biosphere ?
  • Limits in modeling open ended evolution in Alife
    (Bedau and Packard 1998)

  • Classify possible evolutionary patterns, with
    criteria that will take into account the degree
    of likeliness of discontinuities and emergences.
  • Those patterns will include classes of the pure
    possible processes that are directly implemented
    within the computational devices, and appear to
    be objects of investigation in computer sciences.

  • , Bedau and Packard (1998) three kinds of
    emergence class II is bounded emergence
    Hollands (1995) GA Echo , as opposed to class
    I, no emergence, in an Echo simulation with no
    selection (what they call Echo neutral shadow),
    and class III is unbounded emergence manifest
    in the phanerozoic fossil records i.e. the
    history of Life.
  • Bounded for Bedau and Packard means that the
    range of adaptations exhibited is somehow finite,
    which is not the case in class III
  • Intuition no new environment to be colonized in
    digital evolution

Channons classification (2002)
  • Artificial selection in the SAGA simulation, 2.
    natural selection of program codes in Rays
    Tierra, which seems a now limited evolution, 3.
    less limited evolution by Channons natural
    selection in Geb simulation
  • Is this class 3 BP class III (phanerozoic

Typology in terms of driving processes
  • No selection. Phase transitions, etc.
  • Gradual evolution. Smooth landscapes, cumulative
    selection, problem of shifting balance theory,
  • Compositional / discontinuous evolution. Moving
    landscapes (not smooth) problems of facilitators
    of evolution (Wagner and Altenberg 1996
    evolvability as constraints on the
    genotype-phenotype map). No fixed optima, hence
    some open ended evolution.

  • Local patterns of evolution can be simulated,
    hence providing candidate processes
  • General pattern of open-ended evolution in
    phanerozoic record is still unmatched (see Taylor
    2004 for a state of the art)
  • No a priori reason for this
  • But it might be that no pure possible process is
    likely to generate this

  • The possibilities provided by those models settle
    the ground for empirically deciding about the
    specificity of life as a this-worldly feature (as
    opposed to  life  by AL theorists)

  • Computational models are not a very general
    domain of which biology would exemplify some
    cases. (Against strong AL claim)
  • On the contrary they mostly provide pure possible
    processes that might causally contribute to
    origin of traits or evolutionary patterns.
  • The class of possible processes being larger than
    the real processes, obviously not all processes
    simulated are likely to be met in actual biology

  • The main difference between algorithms and
    biology might not be the chemical implementation
    of earthly life (replicators are DNA etc), but
    the fact that processes at work in biology are
    never pure in the sense that they involve all the
    levels of the hierarchy

  • Algorithmic devices only permit to single out one
    or few entities within them. In this sense they
    are only generating the pure processes involving
    solely those entities.
  • This constrains the form of the validation
    problem for computer simulations in evolutionary
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