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Evolving a Disjunctive Predator Prey Swarm using PSO: Adapting Swarms with Swarms

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dc.contributor.advisor Maurer, Peter M.
dc.contributor.advisor Marks, Robert J.
dc.contributor.author Riyaz, Firasath
dc.contributor.other Baylor University. Dept. of Computer Science.
dc.date.copyright 2005.
dc.identifier.uri http://hdl.handle.net/2104/1465
dc.description.abstract Swarm Intelligence is the study of "the emergent collective intelligence of groups of simple agents." Recent research has explored the important applications in the field of business, telecommunications, robotics and optimization. This thesis models a simple prey swarm disjunctively, i.e. a number of disjoint survival attributes are aggregated into a single response. The swarm was initialized using heuristics. We studied the ability of this swarm to evolve its performance using a particle swarm optimization on the disjunctive rules. The rules are characterized through use of a fuzzy inference engine and the rules adapted through changing of the rule membership functions. The result was both improved performance and unexpected emergent behavior for example individual members of the prey swarm began to sacrifice their life to lengthen the life of the swarm aggregate. The disjunctive swarm is found to be robust against rule failures. 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
dc.subject Swarm Intelligence. en
dc.subject Fuzzy logic. en
dc.subject Behavior evolution. en
dc.title Evolving a Disjunctive Predator Prey Swarm using PSO: Adapting Swarms with Swarms en
dc.type Thesis en
dc.description.degree M.S. en
dc.rights.accessrights Worldwide access en
dc.contributor.department Computer Science. en


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