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Learning genetic algorithm parameters using hidden Markov models
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DOI：10.1016/j.ejor.2005.04.045.png)
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摘要
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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. (c) 2005 Elsevier B.V. All rights reserved.
Keyword:
artificial intelligence
evolutionary computations
evolutionary process
adaptive agents
genetic algorithms
heuristics
Markov processes
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6
论文数:
2.2W
被引数:
6.4W
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