Paper Review - 1

From: Ravi Kiran (kiran@cs.washington.edu)
Date: Tue Nov 30 2004 - 23:20:05 PST

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            "THE EVOLUTIONARY ORGIN OF COMPLEX FEATURES"
                    - Richard.E.Lenski, Charles Ofria, Robert T.Pencock,
    Christoph Adami

    One-line summary of the paper:
            This paper explores the possibility of using digital organisms --
    computer programs that self-replicate, mutate and compete -- thereby
    obtaining descendant populations as the consequence of an evolution
    mechanism that parallels the biological analogue and through a series of
    experiments, show that such organisms provide opportunities to address
    important issues in evolutionary biology.

    Two most important ideas in the paper and why:

    Idea 1: Using a digital organism with a fairly simple set of instructions
    can be used to demonstrate the validity of hypothesis, which is also
    supported by comparitive and experimental evidence, that complex features
    of organic forms evolve by modifying structures and functions. This is
    helpful in addressing issues in evolutionary biology for studying problems
    that are difficult to study with organic forms (e.g. evolutionary trace of
    complex organs such as eye) owing to incomplete information, insufficient
    time and impracticality of such experiments.

    Idea 2: Choosing the mode of reproduction to be asexual enabled selection
    to occur among organisms rather than genes, as in the sexual case, where
    there is a distinct possibility of a "dominant" organism enabling a
    harmful mutation to persist for a longer time than it would have in the
    case of asexual reproduction. This is important because asexuality permits
    beneficial combinations of mutations to spread via organisms even though
    they are individually harmful while weeding out organisms deemed harmful.

    Flaw(s) in the paper:

            In the discussion on the Avida system, the authors give an example
    using nand and eql. The idea of a no-op instruction ( an instruction which
    locks the contents of thp particular register ) modifying something was
    confusing. The example could have been a bit more clearly presented.

            The authors observe that the space of solutions is only a tiny
    fraction of the total genotypic search space. However, there is no mention
    of any attempts to incorporate additional randomness into the algorithm
    which would assist in increasing the solution pool. For example, simulated
    annealing is a local search algorithm which is suitably random in its
    behavior and employs a 'cooling' schedule which can be made adaptive.
    Evolutionary biology subscribes to the fact that great upheavals ( e.g.:
    onset of Ice Age ) provide significant changes in the evolutionary
    mechanism. Simulated annealing can be used to of as 'shaking' the table of
    evolutionary search space and letting the spheres of fit organisms roll
    into appropriate slots. Such algorithms should also be explored to enhance
    the effectiveness of genetic algorithms.

    Is the problem and solution just another instance of search ? What, if
    anything, makes this search problem different from the usual search
    problem ?

            In some sense, it is a search. There is a start state ( starting
    organism) and there is an objective function according to which future
    states(descendants) are spawned. The goal states are the organisms that
    ultimately survive. However, the generation of successors involves a
    mutation from among the existing states. Also, evolution can also involve
    moves which are sideways and which regress and yet are still important.


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