Paper Review

From: Gaurav Bhaya (gbhaya@cs.washington.edu)
Date: Tue Nov 30 2004 - 21:43:50 PST

  • Next message: Mathias Ganter: "Paper Review: The evolutionary origin of complex features"

    The Evolutionary origin of complex features

    Richard E. Lenski, Charles Ofria, Robert T. Pennock, Christoph Adami

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    One Line Summary:

    - This paper verifies Darwin's theory of evolution in the context of
    Digital

    organisms which evolve to perform complex functions starting with simple

    ones.

     

    Important Ideas:

    - The paper revolves around the main idea of evolution of digital
    organisms

    which consist of simple operations. The paper shows that evolution
    operations

    involving reproduction, mutation, inheritance, competition etc. can lead
    to

    organisms performing complex functions.

    - The paper shows that rewards for simple functions is necessary in
    order

    to evolve into complex functions such as EQU which involves at least 19

    operations.

     

    Flaws:

    - The paper does mention that rewards for simpler functions are
    necessary

    in the evolution of complex functions. However, it does not mention the

    way of selecting the simpler functions. In particular, as pointed out
    that

    some of the simpler functions are lost in the process of obtaining the
    EQU

    function. The paper does not mention the selection criterion for these

    reward functions. Furthermore, does the choice of these functions
    matter?

    - The paper uses asexual method of reproduction for generating new
    members

    of the population. Although it works well in this case, the paper does
    not

    mention and particular reason for choosing one form over another.
    Moreover,

    I did not understand why the population size matters if the reproduction

    is asexual; i.e., how do other members of the population affect the

    development of one particular gene except for the termination condition.

    - The paper presents lots of number indicating various quantities such

    as pivot point etc however does not elaborate on the significance of
    these

    quantities.

     

    More Questions:

    - How does this technique compare to existing techniques, similar ones

    such as "Genetic Algorithms" or popular search methods such as simulated

    annealing etc.

    - What would be the effect of choosing Sexual reproduction as opposed to

    asexual reproduction.

     

    Open Questions:

    - Can this idea be applied to other search problems? Does it work

    as well or better than known search techniques for the problems -- If
    yes,

    then we have a one size fits all solution.

    - How can this technique be applied to problems where solution or
    "simpler

    problems" are not known? How would one determine that a solution has
    been

    found -- In particular how do we know that a program does what it is
    intended

    to do.

     

    The paper seems to discuss a new search technique based on Darwin's
    theory

    of evolution. The idea of rewards based on the fitness of the current

    population to drive further search is used as a heuristic to guide the

    search.

     

     

    -- Gaurav


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