OPTIMIZATION OF THE GENETIC ALGORITHM IN THE SHEHERAZADE WARGAMING SIMULATOR

Persistent Link:
http://hdl.handle.net/10150/203449
Title:
OPTIMIZATION OF THE GENETIC ALGORITHM IN THE SHEHERAZADE WARGAMING SIMULATOR
Author:
Momen, Faisal
Issue Date:
2011
Publisher:
The University of Arizona.
Rights:
Copyright © 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.
Abstract:
Stability 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.
Type:
text; Electronic Dissertation
Keywords:
Electrical & Computer Engineering
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Electrical & Computer Engineering
Degree Grantor:
University of Arizona
Advisor:
Rozenblit, Jerzy W.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleOPTIMIZATION OF THE GENETIC ALGORITHM IN THE SHEHERAZADE WARGAMING SIMULATORen_US
dc.creatorMomen, Faisalen_US
dc.contributor.authorMomen, Faisalen_US
dc.date.issued2011-
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.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.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectElectrical & Computer Engineeringen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical & Computer Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorRozenblit, Jerzy W.en_US
dc.contributor.committeememberAkoglu, Alien_US
dc.contributor.committeememberLysecky, Romanen_US
dc.contributor.committeememberRozenblit, Jerzy W.en_US
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