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Information storage capacity of genetic algorithm fitness maps.

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dc.contributor.advisor Hamerly, Gregory James, 1977-
dc.contributor.author Montañez, George D.
dc.date.copyright 2011-08
dc.identifier.uri http://hdl.handle.net/2104/8233
dc.description.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. en_US
dc.publisher en
dc.rights Baylor University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact librarywebmaster@baylor.edu for inquiries about permission. en_US
dc.subject Genetic algorithms. en_US
dc.subject Fitness functions. en_US
dc.subject Information theory. en_US
dc.subject Storage complexity. en_US
dc.title Information storage capacity of genetic algorithm fitness maps. en_US
dc.type Thesis en_US
dc.description.degree M.S. en_US
dc.rights.accessrights Worldwide access. en_US
dc.rights.accessrights Access changed 3/14/13.
dc.contributor.department Computer Science. en_US
dc.contributor.schools Baylor University. Dept. of Computer Science. en_US


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