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Evolutionary computation methods and their applications in statistics

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  • Francesco Battaglia

Abstract

A brief discussion of the genesis of evolutionary computation methods, their relationship to artificial intelligence, and the contribution of genetics and Darwin’s theory of natural evolution is provided. Then, the main evolutionary computation methods are illustrated: evolution strategies, genetic algorithms, estimation of distribution algorithms, differential evolution, and a brief description of some evolutionary behavior methods such as ant colony and particle swarm optimization. We also discuss the role of the genetic algorithm for multivariate probability distribution random generation, rather than as a function optimizer. Finally, some relevant applications of genetic algorithm to statistical problems are reviewed: selection of variables in regression, time series model building, outlier identification, cluster analysis, design of experiments

Suggested Citation

  • Francesco Battaglia, 2009. "Evolutionary computation methods and their applications in statistics," Statistica, Department of Statistics, University of Bologna, vol. 69(2), pages 201-224.
  • Handle: RePEc:bot:rivsta:v:69:y:2009:i:2:p:201-224
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