Effects of a deterministic hill climber on hBOA

Authors:
Elizabeth Radetic;Martin Pelikan;David E. Goldberg
Affiliations:
University of Missouri at St. Louis, St. Louis, MO, USA;University of Missouri at St. Louis, St. Louis, MO, USA;University of Illinois at Urbana-Champaign, Urbana, IL, USA
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

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Abstract

Hybridization of global and local search algorithms is a well-established technique for enhancing the efficiency of search algorithms. Hybridizing estimation of distribution algorithms (EDAs) has been repeatedly shown to produce better performance than either the global or local search algorithm alone. The hierarchical Bayesian optimization algorithm (hBOA) is an advanced EDA which has previously been shown to benefit from hybridization with a local searcher. This paper examines the effects of combining hBOA with a deterministic hill climber (DHC). Experiments reveal that allowing DHC to find the local optima makes model building and decision making much easier for hBOA. This reduces the minimum population size required to find the global optimum, which substantially improves overall performance.