Information storage capacity of genetic algorithm fitness maps.

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Masters

Grantor

Baylor University.

Year dc:date.issued

2011

Author dc:creator

• Montañez, George D.

Advisor dc:contributor.advisor

• Hamerly, Gregory James, 1977-

• Genetic algorithms.
• Fitness functions.
• Information theory.
• Storage complexity.

Statement dc:rights

• Baylor University works 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 libraryquestions@baylor.edu for inquiries about permission.

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/2104/8233

OAI identifier oai:identifier

oai:baylor-ir.tdl.org:2104/8233