Masters Theses

Keywords and Phrases

Meta-evolutionary algorithm

Abstract

"Traditional evolutionary algorithms (EAs) are powerful robust problem solvers that have several fixed parameters which require prior specification. Having to determine good values for any of these parameters can be problematic, as the performance of EAs is generally very sensitive to these parameters, requiring expert knowledge to set optimally without extensive use of trial and error. Parameter control is a promising approach to achieving this automation and has the added potential of increasing EA performance based on both theoretical and empirical evidence that the optimal values of EA strategy parameters change during the course of executing an evolutionary run. While many methods of parameter control have been published that focus on removing the population size parameter (µ), most of these methods have undesirable side effects for doing so. This thesis starts by providing evidence for the benefits of making a dynamic parameter and then introduces two novel methods for removing the need to preset µ. These methods are then compared, explaining the strengths and weaknesses of each. The benefit of employing a dynamic value for µ is demonstrated on two test problems through the use of a meta-EA, and the first novel method is shown to be useful on several binary test problems while the second performs well on a real valued test problem"--Abstract, page iii.

Advisor(s)

Tauritz, Daniel R.

Committee Member(s)

Frank, Ronald L.
Wilkerson, Ralph W.

Department(s)

Computer Science

Degree Name

M.S. in Computer Science

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2010

Pagination

x, 43 pages

Note about bibliography

Includes bibliographical references.

Rights

© 2010 Jason Cook, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Library of Congress Subject Headings

Control theory -- Computer programs
Evolutionary computation
Evolutionary programming (Computer science)
Mathematical optimization

Thesis Number

T 9663

Print OCLC #

688999326

Electronic OCLC #

748374921

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