Scalability of the Bayesian optimization algorithm

Martin Pelikán

doi:10.1016/S0888-613X(02)00095-6

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Scalability of the Bayesian optimization algorithm

2002-11-01

Martin Pelikán

Kumara Sastry

David E. Goldberg

DOI：10.1016/S0888-613X(02)00095-6

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Abstract

En 中文

To solve a wide range of different problems, the research in black-box optimization faces several important challenges. One of the most important challenges is the design of methods capable of automatic discovery and exploitation of problem regularities to ensure efficient and reliable search for the optimum. This paper discusses the Bayesian optimization algorithm (BOA), which uses Bayesian networks to model promising solutions and sample new candidate solutions. Using Bayesian networks in combination with population-based genetic and evolutionary search allows BOA to discover and exploit regularities in the form of a problem decomposition. The paper analyzes the applicability of the methods for learning Bayesian networks in the context of genetic and evolutionary search and concludes that the combination of the two approaches yields robust, efficient, and accurate search. (C) 2002 Elsevier Science Inc. All rights reserved.

Keywords:

genetic and evolutionary computation

graphical models

probabilistic model-building genetic algorithms

black-box optimization

decomposition

Bayesian optimization algorithm