Extending Population-Based Incremental Learning to Continuous Search Spaces (1998)
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BibTeX
@MISC{Sebag98extendingpopulation-based,
author = {Michèle Sebag and Antoine Ducoulombier},
title = {Extending Population-Based Incremental Learning to Continuous Search Spaces},
year = {1998}
}
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Abstract
. 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. 1 Introduction Evolutionary algorithms (EAs) [13, 6, 5] are mostly used to find the optima of some fitness function F defined on a search space\Omega . F :\Omega ! IR From a machine learning (ML) perspective [9], evolution is similar to learning by query: Learning by query starts with a void hypothesis and gradually refines the current hypothesis through asking questions to some oracle. In ML, ...
