Learning genetic algorithm parameters using hidden Markov models

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.04.045 Byline: Jackie Rees (a), Gary J. Koehler (b) Keywords: Artificial intelligence; Evolutionary computations; Evolutionary process; Adaptive agents; Genetic algorithms; Heuristics; Markov processes 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. Author Affiliation: (a) Krannert Graduate School of Management, 403 West State Street, Purdue University, West Lafayette, IN 47907-2056, USA (b) Department of Decision and Information Sciences, Warrington College of Business, University of Florida, P.O. Box 117169, Gainesville FL 32611-7169, USA Article History: Received 7 October 2003; Accepted 14 April 2005