The evolutionary origin of complex features

From: Stef Sch... (stef@cs.washington.edu)
Date: Wed Dec 01 2004 - 11:33:25 PST

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    Paper Title: The evolutionary origin of complex features
    Authors: Richard E. Lenski, Charles Ofria, Robert T. Pennock,
    Christoph Adaml

    This paper examines how complex features can arise in digital organisms
    through just random mutation and natural selection, and uses these
    simulations to postulate how complex features such as our visual and
    auditory systems might occur/evolve in nature.

    Strengths:
    One of the main contributions of this paper is it's empirical support
    of Darwin's theory of evolution. Unlike the real world, in this
    digital one they have complete knowledge of all ancestors, and how each
    offspring evolves from its ancestors. One of the biggest complains of
    evolution is that complex features, such as our eyes, are unlikely to
    evolve naturally. For example, a random mutation might cause a simple
    light sensing cell to develop, but without any nerves to connect it to
    the brain, it would be useless, and therefor no more (and possibly
    less, due to it's added complexity) fit to pass that trait on. Their
    experiments show that the equals function, which is relatively complex
    in their digital system, will consistently evolve using only basic
    building blocks.

    Furthermore they show that a deleterious mutation at one step may
    actually enable a complex operation to evolve in a subsequent
    generation, thus at least providing some argument as to how complex
    features can evolve in spite (and sometime because of) of harmful
    random mutations.

    Finally, they provide some evidence that intermediate rewards are
    necessary. The equals function builds off of several less complex
    functions (such as NAND, XOR, NOT, etc.) In experiments where most/all
    of these intermediate functions were rewarded the equals function
    consistently evolved and became dominant. However, in experiments
    where none of these intermediate functions were rewarded, the equals
    function never evolved. This lends some credence to the believe that
    complex systems evolve from a sequence of simple increases in fitness.
    Namely, it would be quite difficult to evolve an eye from nothing, but
    a simple light/dark sensing cell would probably be advantageous. This
    gives evidence that small advantages are a requisite for the evolution
    of complex features.

    Flaws:
    They used a strange metric of deleting one instruction at a time to
    determine how many instructions were needed for the organism to perform
    certain functions. Namely, they didn't analyze the how complex
    interactions/redundant may affect the computation of equals (indeed,
    one organism apparently needed only 17 instructions to calculate
    equals, whereas the best believed one requires 19, so clearly there are
    some complex interactions taking place).

    Secondly, some of the data was a bit lacking. They only analyzed one
    sample as a case study, and in their analysis they didn't look at the
    evolutionary steps leading up to the evolution of equals they merely
    looked at the previous step. They stated that it evolved from several
    different methods, but it would be interesting to see exactly what was
    requisite and what was not for equals to evolve.

    Finally, they should have analyzed why the equals function didn't arise
    in the experiments where it didn't. Given sufficient time, one would
    expect this system to evolve the equals function since it is so
    beneficial. If the population got stuck in some local optimum, such
    that the transition to equals would require too many deleterious steps,
    that would be an interesting result and have some potential
    ramifications for how we understand the set of species in the world
    today. This would be an interesting study, and might shed light onto
    why humans only see the visible spectrum, and not infra-red or
    ultraviolet.

    Relation to Search:
    This is clearly a simulated annealing type search problem. The
    objective function is the weighted number of functions each organism
    calculates, further weighted by the program's complexity. The space is
    the space of all programs using these 26 instructions. The operators
    are copying with mutations. The start state is the grid initialized
    with programs that can only copy themselves, and the goal states is a
    program that can achieve the maximum objective function (i.e. one that
    can calculate all of the simple functions).

    It is slightly different than standard search, in that each of the
    programs is competing against the others instead of exploring the space
    with others.

    Open research questions:
    One of the key open questions posed at the end of this is that of
    sexual reproduction. It is generally assumed that sexual reproduction
    helps, and that it would allow beneficial features to be shared. My
    question is, would this benefit the ability to generate equals, or
    would it encourage the population to get stuck in some local optimum,
    since a single beneficial feature might propagate through the
    population, but the changes from it to equals may be too great for such
    descendants to survive.

    Finally, is this how nature actually works? The authors give some
    justification as to why they believe this is at least similar to how
    nature works, but this is an open question. Unfortunately, these
    questions take an incredibly long time to test in nature, so we'll
    probably never know.


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