Masters Theses

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, page iii.

Advisor(s)

Tauritz, Daniel R.

Committee Member(s)

Grow, David E.
McMillin, Bruce M.

Department(s)

Computer Science

Degree Name

M.S. in Computer Science

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2008

Pagination

viii, 69 pages

Note about bibliography

Includes bibliographical references (pages 140-142).

Rights

© 2008 Travis Service, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Library of Congress Subject Headings

Coevolution -- Mathematical models
Evolutionary computation
Evolutionary programming (Computer science)
Mathematical optimization

Thesis Number

T 9357

Print OCLC #

260325916

Electronic OCLC #

226300638

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