Adaptive global optimization with local search
Adaptive global optimization with local search
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Proceedings of the 9th annual conference on Genetic and evolutionary computation
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Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
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Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
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Entropy-based substructural local search for the bayesian optimization algorithm
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PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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This paper presents an approach to combine competent crossover and mutation operators via probabilistic model building. Both operators are based on the probabilistic model building procedure of the extended compact genetic algorithm (eCGA). The model sampling procedure of eCGA, which mimics the behavior of an idealized recombination---where the building blocks (BBs) are exchanged without disruption---is used as the competent crossover operator. On the other hand, a recently proposed BB-wise mutation operator---which uses the BB partition information to perform local search in the BB space---is used as the competent mutation operator. The resulting algorithm, called hybrid extended compact genetic algorithm (heCGA), makes use of the problem decomposition information for (1) effective recombination of BBs and (2) effective local search in the BB neighborhood. The proposed approach is tested on different problems that combine the core of three well known problem difficulty dimensions: deception, scaling, and noise. The results show that, in the absence of domain knowledge, the hybrid approach is more robust than either single-operator-based approach.