Learning genetic algorithm parameters using hidden Markov models

Learning genetic algorithm parameters using hidden Markov models

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Article ID: iaor20084083
Country: Netherlands
Volume: 175
Issue: 2
Start Page Number: 806
End Page Number: 820
Publication Date: Dec 2006
Journal: European Journal of Operational Research
Authors: ,
Keywords: markov processes, stochastic 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.

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