Extending population-based incremental learning to continuous search spaces

Abstract

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An alternative to Darwinian-like artificial evolution is offered by Population-Based Incremental Learning (PBIL): this algorithm memorizes the best past individuals and uses this memory as a distribution, to generate the next population from scratch. This paper extends PBIL from boolean to continuous search spaces. A Gaussian model is used for the distribution of the population. The center of this model is constructed as in boolean PBIL. Several ways of defining and adjusting the variance of the model are investigated. The approach is validated on several large-sized problems.