Paper 1 Review

From: Vaishnavi Sannidhanam (vaishu@cs.washington.edu)
Date: Wed Dec 01 2004 - 01:19:11 PST

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    Summary:
    --------------
    The paper applies the Darwinian evolutionary theory to the realm of digital
    organisms. It studies how complex operations evolve from basic operations
    and how variations and mutations to the basic operations gradually lead to
    the development of complex ones. It explains how replication, variation and
    differential fitness results in the development of better digital organisms.

    Important Ideas:
    ----------------------
    The idea of using Darwinian evolution for the evolution of digital organisms
    is very clever. As the idea is very simple - give high points to beneficial
    and complex functions once they have been developed to help sustain them and
    to give low points to harmful and useless functions to help weed them out -
    is easy to understand and implement.

    The paper not only reasons out why the theory of evolution (concept of
    rewarding certain functionalities and punishing certain others) in this case
    works, but also presents statistical evidence as to how this works, thus
    making the study complete.

    The paper also chooses asexual reproduction as opposed to sexual
    reproduction, which not only reduces the complexity of the algorithm (how to
    choose partners to get the best offspring) but also at the same time avoids
    situations where selection of wrong partners might cause (like bad
    offspring/bad functionalities persisting over generations) .

    The paper also shows how sometimes what is termed as a bad mutation (loosing
    NAND functionality) at one generation might later on result in something
    very good for the next generations to come (EQU).

    Flaws:
    ----------
    The paper though briefly mentions the concept of sexual reproduction, it
    does not really explain how this would have effected the development of EQU.
    It does not do any comparative analysis of asexual vs. sexual reproduction
    thus leaving its audience wondering how evolution in each case would have
    been different.

    Though the paper mentions that evolution of complex functions in most cases
    resulted in the loss of a basic functionality for the parent, the paper does
    not really explain how the loss of simple functions helped in the evolution
    of complex functions. It also leaves us to the question of how the simple
    functions should be weighted in order for the complex functions to develop
    quickly.

    The paper could have also talked about what introduction of high randomness
    into the algorithms would do to the evolution of the functions.

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

    It can be categorized as a search problem where we start out with minimal
    functionality and try to reach our goal state of high functionality.
    However, the process of doing the search is a little different as some
    mutations might result in regressing back to older search states and taking
    a different path to reach the goal state.


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