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dc.contributor.advisorRozenblit, Jerzy W.en_US
dc.contributor.authorMomen, Faisal
dc.creatorMomen, Faisalen_US
dc.date.accessioned2012-01-17T16:40:18Z
dc.date.available2012-01-17T16:40:18Z
dc.date.issued2011
dc.identifier.urihttp://hdl.handle.net/10150/203449
dc.description.abstractStability and Support Operations (SASO) continue to play an important role in modern military exercises. The Sheherazade simulation system was designed to facilitate SASO-type mission planning exercises by rapidly generating and evaluating hundreds of thousands of alternative courses-of-action (COAs). The system is comprised of a coevolution engine that employs a Genetic Algorithm (GA) to generate the COAs for each side in a multi-sided conflict and a wargamer that models various subjective factors such as regional attitudes and faction animosities to evaluate their effectiveness. This dissertation extends earlier work on Sheherazade, in the following ways: 1) The GA and coevolution framework have been parallelized for improved performance on current multi-core platforms 2) the effects of various algorithm parameters, both general and specific to Sheherazade, were analyzed 3) alternative search techniques reflecting recent developments in the field have been evaluated for their capacity to improve the quality of the results.
dc.language.isoenen_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.subjectElectrical & Computer Engineeringen_US
dc.titleOPTIMIZATION OF THE GENETIC ALGORITHM IN THE SHEHERAZADE WARGAMING SIMULATORen_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberAkoglu, Alien_US
dc.contributor.committeememberLysecky, Romanen_US
dc.contributor.committeememberRozenblit, Jerzy W.en_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical & Computer Engineeringen_US
thesis.degree.namePh.D.en_US
refterms.dateFOA2018-06-27T19:10:10Z
html.description.abstractStability and Support Operations (SASO) continue to play an important role in modern military exercises. The Sheherazade simulation system was designed to facilitate SASO-type mission planning exercises by rapidly generating and evaluating hundreds of thousands of alternative courses-of-action (COAs). The system is comprised of a coevolution engine that employs a Genetic Algorithm (GA) to generate the COAs for each side in a multi-sided conflict and a wargamer that models various subjective factors such as regional attitudes and faction animosities to evaluate their effectiveness. This dissertation extends earlier work on Sheherazade, in the following ways: 1) The GA and coevolution framework have been parallelized for improved performance on current multi-core platforms 2) the effects of various algorithm parameters, both general and specific to Sheherazade, were analyzed 3) alternative search techniques reflecting recent developments in the field have been evaluated for their capacity to improve the quality of the results.


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