Effective linkage learning using low-order statistics and clustering

Authors:
Leonardo Ramos Emmendorfer;Aurora Trinidad Ramirez Pozo
Affiliations:
Centro de Ciencias Computacionais, Federal University of Rio Grand, Rio Grande, RS, Brazil;Department of Computer Science and Numerical Methods for Engineering, Federal University of Paranà, Paranà, Brazil
Venue:
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Year:
2009

Citing 15
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Abstract

The adoption of probabilistic models for selected individuals is a powerful approach for evolutionary computation. Probabilistic models based on high-order statistics have been used by estimation of distribution algorithms (EDAs), resulting better effectiveness when searching for global optima for hard optimization problems. This paper proposes a new framework for evolutionary algorithms, which combines a simple EDA based on order 1 statistics and a clustering technique in order to avoid the high computational cost required by higher order EDAs. The algorithm uses clustering to group genotypically similar solutions, relying that different clusters focus on different substructures and the combination of information from different clusters effectively combines substructures. The combination mechanism uses an information gain measure when deciding which cluster is more informative for any given gene position, during a pairwise cluster combination. Empirical evaluations effectively cover a comprehensive range of benchmark optimization problems.