Linkage Problem , Distribution Estimation , and Bayesian

In this paper, an algorithm based on the concepts of genetic algorithms that uses an estimation of the joint distribution of promising solutions in order to generate new candidate solutions is proposed. The algorithm is settled into the context of evolutionary computation and the algorithms based on the estimation of distributions. The proposed algorithm is called the Bayesian optimization algorithm (BOA). To estimate the distribution of promising solutions, the techniques for modeling multivariate data by Bayesian networks are used. The proposed algorithm identiies, reproduces and mixes building blocks up to a speciied order. It is independent of the ordering of the variables in strings representing the solutions. Moreover, prior information about the problem can be incorporated into the algorithm. However, the prior information is not essential. The rst experiments were done with additively decomposable problems with non-overlapping building blocks. The proposed algorithm is able to solve all tested problems in linear or close to linear time with respect to the problem size without the use of any prior knowledge about the problem.

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