Real-Coded Bayesian Optimization Algorithm: Bringing the Strength of BOA into the Continuous World

This paper describes a continuous estimation of distribution algorithm (EDA) to solve decomposable, real-valued optimization problems quickly, accurately, and reliably. This is the real-coded Bayesian optimization algorithm (rBOA). The objective is to bring the strength of (discrete) BOA to bear upon the area of real-valued optimization. That is, the rBOA must properly decompose a problem, efficiently fit each subproblem, and effectively exploit the results so that correct linkage learning even on nonlinearity and probabilistic building-block crossover (PBBC) are performed for real-valued multivariate variables. The idea is to perform a Bayesian factorization of a mixture of probability distributions, find maximal connected subgraphs (i.e. substructures) of the Bayesian factorization graph (i.e., the structure of a probabilistic model), independently fit each substructure by a mixture distribution estimated from clustering results in the corresponding partial-string space (i.e., su...

• Type: Conference
• Year: 2004
• Where: GECCO
• Authors: Chang Wook Ahn, Rudrapatna S. Ramakrishna, David E. Goldberg
• Keywords: Bayesian Factorization, Bayesian Optimization Algorithm, GECCO 2004, Real-valued Optimization