Kumara Sastry

Evolutionary algorithms + graphical models = scalable black-box optimization

Pelikan, M., Sastry, K., Goldberg, D. E. (2001). IlliGAL report no. 2001029. University of Illinois at Urbana-Champaign. [Full paper - PDF] [Full paper - PS].

Abstract:

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 automatically discovering the regularities in the problem and utilizing these to ensure efficient and reliable search. This paper discusses the Bayesian optimization algorithm (BOA) that uses Bayesian networks to model promising solutions and guide exploration of the search space. Using Bayesian networks in combination with population-based genetic and evolutionary search allows the algorithm to discover and utilize regularities in the form of problem decomposition. The paper analyzes the applicability of the methods for learning Bayesian networks in context of genetic and evolutionary search. In particular, the population sizing ensuring that BOA learns a proper decomposition of the problem is analyzed. The paper concludes that the combination of the two approaches in BOA yields a robust, efficient, and accurate search.

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