Learning genetic algorithm parameters using hidden Markov models [An article from: European Journal of Operational Research]

This digital document is a journal article from European Journal of Operational Research, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
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.