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Title: Co-optimization: a generalization of coevolution
Author (s): Service, Travis, 1985-
Advisor(s): Tauritz, Daniel R.
Issue Date: 2008
Publisher: Missouri University of Science and Technology
Citation: Service, Travis. "Co-Optimization: A Generalization of Coevolution." Master's Thesis, Computer Science, Missouri University of Science and Technology, 2008.
Abstract: "Many problems encountered in computer science are best stated in terms of interactions amongst individuals. For example, many problems are most naturally phrased in terms of finding a candidate solution which performs best against a set of test cases. In such situations, methods are needed to find candidate solutions which are expected to perform best over all test cases. Coevolution holds the promise of addressing such problems by employing principles from biological evolution, where populations of candidate solutions and test cases are evolved over time to produce higher quality solutions...This thesis presents a generalization of coevolution to co-optimization, where optimization techniques that do not rely on evolutionary principles may be used. Instead of introducing a new addition to coevolution in order to make it better suited for a particular class of problems, this thesis suggests removing the evolutionary model in favor of a technique better suited for that class of problems"--Abstract, p. iii.
Type: Thesis/Dissertation
text
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titleCo-optimization: a generalization of coevolution
contributor.advisorTauritz, Daniel R.
contributor.authorService, Travis, 1985-
subject.LCSHCoevolution -- Mathematical models.
subject.LCSHEvolutionary computation.
subject.LCSHEvolutionary programming (Computer science)
subject.LCSHMathematical optimization.
date.issued2008
publisherMissouri University of Science and Technology
identifier.URI
http://scholarsmine.mst.edu/thesis/pdf/Service_09007dcc804e2264.pdf
identifier.citationService, Travis. "Co-Optimization: A Generalization of Coevolution." Master's Thesis, Computer Science, Missouri University of Science and Technology, 2008.
identifier.oclc226300638
descriptionIncludes bibliographical references (p. 65-68).
descriptionMode of access: World Wide Web.
descriptionSystem requirements: Adobe Acrobat Reader; Internet browser.
descriptionThe entire thesis text is included in file.
descriptionThesis (M.S.)--Missouri University of Science and Technology, 2008.
descriptionTitle from title screen of thesis/dissertation PDF file (viewed April 26, 2008)
descriptionVita.
description.abstract"Many problems encountered in computer science are best stated in terms of interactions amongst individuals. For example, many problems are most naturally phrased in terms of finding a candidate solution which performs best against a set of test cases. In such situations, methods are needed to find candidate solutions which are expected to perform best over all test cases. Coevolution holds the promise of addressing such problems by employing principles from biological evolution, where populations of candidate solutions and test cases are evolved over time to produce higher quality solutions...This thesis presents a generalization of coevolution to co-optimization, where optimization techniques that do not rely on evolutionary principles may be used. Instead of introducing a new addition to coevolution in order to make it better suited for a particular class of problems, this thesis suggests removing the evolutionary model in favor of a technique better suited for that class of problems"--Abstract, p. iii.
description.
statementOfResponsibility
by Travis Service.
typeThesis/Dissertation
type.DCMITypetext
rightsThese materials are protected under copyright by the original author.
language.ISO639-2eng
format.extentviii, 69 p. : ill., digital, PDF file.
date.accessioned2008-04-16T22:46:56Z
date.available2008-04-25T19:42:45Z
identifier.persist.URI
http://scholarsmine.mst.edu/thesis/Cooptimization_a_g_09007dcc804eb77d.html
Full Text
Service_09007dcc804e2264.pdf