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    Information storage capacity of genetic algorithm fitness maps.

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    Access changed 3/14/13.
    Date
    2011-09-14
    Author
    Montañez, George D.
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    Abstract
    To accurately measure the amount of information a genetic algorithm can generate, we must first measure the amount of information one can store, using a fitness map. The amount of information generated, minus the storage capacity, gives a tighter estimate on the levels of information generated by genetic algorithms. To measure the information storage capacity of fitness maps, we use the method suggested by Abu-Mostafa et al. (Abu-Mostafa and St Jacques, 1985) for measuring the information storage capacity of general forms of memory. Additionally, we measure the information in reference to the active information metric, as developed by Dembski et al. (Dembski and Marks, 2009). Our results show that a number of bits linear in the size of the search space can be stored in a fitness map, but only a logarithmic number of bits can be extracted by a genetic algorithm with stabilizing population and fixed population size.
    URI
    http://hdl.handle.net/2104/8233
    Department
    Computer Science.
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    • Electronic Theses and Dissertations
    • Theses/Dissertations - Computer Science

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    Copyright © Baylor® University All rights reserved. Legal Disclosures.
    Baylor University Waco, Texas 76798 1-800-BAYLOR-U
    Baylor University Libraries | One Bear Place #97148 | Waco, TX 76798-7148 | 254.710.2112 | Contact: libraryquestions@baylor.edu
    If you find any errors in content, please contact librarywebmaster@baylor.edu
    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    TDL
    Theme by 
    Atmire NV