Efficient Estimation of Distribution Algorithms by Using the Empirical Selection Distribution

作者: S. Ivvan , Arturo Hernndez , Salvador Botello

DOI: 10.5772/8056

关键词:

摘要: Estimation of Distribution Algorithms (EDAs) (Muhlenbein et al., 1996; Muhlenbein & PaaB, 1996) are a promising area research in evolutionary computation. EDAs propose to create models that can capture the dependencies among decision variables. The widely known Genetic Algorithm could benefit from available if building blocks solution were correlated. However, it was proved genetic algorithm have limited capacity for discovering and using complex relationships (correlations) instead, focus on learning probability distributions which serve as vehicle data structure well. In order show how proposed method unifies theory infinite sized population with finite case practical EDAs, we explain them first. An EDA would perform steps shown Table 1.

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