Bayesian optimization algorithm: from single level to hierarchy
Bayesian optimization algorithm: from single level to hierarchy
Linkage learning, overlapping building blocks, and systematic strategy for scalable recombination
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A crossover for complex building blocks overlapping
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Linkage identification based on epistasis measures to realize efficient genetic algorithms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population sizing for entropy-based model building in discrete estimation of distribution algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Dependency structure matrix, genetic algorithms, and effective recombination
Evolutionary Computation
Linkage learning by number of function evaluations estimation: Practical view of building blocks
Information Sciences: an International Journal
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Existing EDAs learn linkages starting from pairwise interactions. The characteristic function which indicates the relations among variables are binary. In other words, the characteristic function indicates that there exist or not interactions among variables. Empirically, it can occur that two variables should be sometimes related but sometimes not. This paper introduces a real-valued characteristic function to illustrate this property of fuzziness. We examine all the possible binary models and real-valued models on a test problem. The results show that the optimal real-valued model is better than all the binary models. This paper also proposes a crossover method which is able to utilize the real-valued information. Experiments show that the proposed crossover could reduce the number of function evaluations up to four times. Moreover, this paper proposes an effective method to find a threshold for entropy based interaction-detection metric and a method to learn real-valued models. Experiments show that the proposed crossover with the learned real-valued models works well.