The Center for Education and Research in Information Assurance and Security (CERIAS)

The Center for Education and Research in
Information Assurance and Security (CERIAS)
Center for Education and Research in Information Assurance and Security

Learning Genetic Algorithm Parameters Using Hidden Markov Models

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Author

J Rees, G Koehler

Tech report number

CERIAS TR 2005-151

Entry type

article

Abstract

Genetic algorithms (GAs) are routinely used to search problem spaces of interest. A lesser known but growing group of applications of GAs is the modeling of so-called “evolutionary processes”, for example, organizational learning and group decision-making. Given such an application, we show it is possible to compute the likely GA parameter settings given observed populations of such an evolutionary process. We examine the parameter estimation process using estimation procedures for learning hidden Markov models, with mathematical models that exactly capture expected GA behavior. We then explore the sampling distributions relevant to this estimation problem using an experimental approach.

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Date

2006

URL

http://www.sciencedirect.com/s ... bccb5e20769ffb495d55f9c94

Journal

European Journal of Operational Research

Key alpha

Bhargava

Number

2

Pages

806-820

Volume

175

Publication Date

2006-00-00

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