Genetic algorithms applied to graph theory

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dc.contributor.advisor Bagga, Jay en_US
dc.contributor.author Anderson, Jon K. en_US
dc.date.accessioned 2011-06-03T19:38:46Z
dc.date.available 2011-06-03T19:38:46Z
dc.date.created 1999 en_US
dc.date.issued 1999
dc.identifier LD2489.Z78 1999 .A53 en_US
dc.identifier.uri http://cardinalscholar.bsu.edu/handle/handle/186610
dc.description.abstract This thesis proposes two new variations on the genetic algorithm. The first attempts to improve clustering problems by optimizing the structure of a genetic string dynamically during the run of the algorithm. This is done by using a permutation on the allele which is inherited by the next generation. The second is a multiple pool technique which ensures continuing convergence by maintaining unique lineages and merging pools of similar age. These variations will be tested against two well-known graph theory problems, the Traveling Salesman Problem and the Maximum Clique Problem. The results will be analyzed with respect to string rates, child improvement, pool rating resolution, and average string age.
dc.description.sponsorship Department of Computer Science
dc.format.extent v, 124 leaves : ill. ; 28 cm. en_US
dc.source Virtual Press en_US
dc.subject.lcsh Genetic algorithms. en_US
dc.subject.lcsh Genetic programming (Computer science). en_US
dc.title Genetic algorithms applied to graph theory en_US
dc.description.degree Thesis (M.S.)
dc.identifier.cardcat-url http://liblink.bsu.edu/catkey/1136714 en_US


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  • Master's Theses [5510]
    Master's theses submitted to the Graduate School by Ball State University master's degree candidates in partial fulfillment of degree requirements.

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