Doctoral Dissertations
The art of parameterless evolutionary algorithms
Abstract
"Evolutionary Algorithms (EAs) can be powerful problem solving tools when other methods fail. EAs maintain a collection of solutions and go through many iterations of recombining and randomly modifying current solutions, deleting some of the discovered solutions and giving the better solutions a higher chance of surviving. In such a manner EAs explore the search space in the pursuit of globally optimal solutions. To make an EA successful in its pursuit of globally optimal solutions on any particular problem, the evolutionary operators and relevant parameters must be carefully tuned. The tuning of operators and parameters is often manual and time consuming. The focus of this dissertation is on automatic tuning of operators and parameters. This dissertation presents methodologies for automating the tuning of the population size parameter and the choice of the mate selection operator"--Abstract, page iii.
Advisor(s)
Tauritz, Daniel R.
Committee Member(s)
McMillin, Bruce M.
Thakur, Mayur
Wilkerson, Ralph W.
Morgan, Ilene H.
Department(s)
Computer Science
Degree Name
Ph. D. in Computer Science
Publisher
Missouri University of Science and Technology
Publication Date
Spring 2008
Pagination
xiv, 103 pages
Note about bibliography
Includes bibliographical references (pages 98-102).
Rights
© 2008 Ekaterina A. Holdener, All rights reserved.
Document Type
Dissertation - Citation
File Type
text
Language
English
Subject Headings
Computer algorithmsDistributed parameter systemsEvolutionary programming (Computer science)Genetic algorithms
Thesis Number
T 9387
Print OCLC #
270996914
Recommended Citation
Holdener, Ekaterina A., "The art of parameterless evolutionary algorithms" (2008). Doctoral Dissertations. 1759.
https://scholarsmine.mst.edu/doctoral_dissertations/1759
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