Doctoral Dissertations


The art of parameterless evolutionary algorithms


"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.


Tauritz, Daniel R.

Committee Member(s)

McMillin, Bruce M.
Thakur, Mayur
Wilkerson, Ralph W.
Morgan, Ilene H.


Computer Science

Degree Name

Ph. D. in Computer Science


Missouri University of Science and Technology

Publication Date

Spring 2008


xiv, 103 pages

Note about bibliography

Includes bibliographical references (pages 98-102).


© 2008 Ekaterina A. Holdener, All rights reserved.

Document Type

Dissertation - Citation

File Type




Subject Headings

Computer algorithms
Distributed parameter systems
Evolutionary programming (Computer science)
Genetic algorithms

Thesis Number

T 9387

Print OCLC #


Link to Catalog Record

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