Hybrid evolutionary algorithm control system based on a model of mixing expert opinions
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Abstract
The control system for hybrid evolutionary algorithm based on mixture of experts’ model is proposed in the article. Basic element of proposed system is two-level model for classification of optimization problems. The model lets to extract maximum of information about the problem, classify it and direct the algorithm via the optimal path
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References
H. Ishibuchi, T. Yoshida, and T. Murata, “Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling”, IEEE Transactions on Evolutionary Computation, vol. 7, no. 2, pp. 204–223, Apr. 2003. DOI:10.1109/TEVC.2003.810752
Titterington О., A. Smith, and E. Makov, StatisticalAnalysis of Finite Mixture Distributions, New York: Wiley, 1985.
S. Khaikin, Neural networks: full course, 2nd ed. Moscow: Publishing House"Williams", 2006, p. 1104.
Y. Kalnibolotsky and O. Khrustavka, “Modifications of genetic algorithms”, Electronics and communications, no. 5, pp. 54–61, 2008.
O. Khrustavka, “Base of rules for choosing a combination of genetic operators for a hybrid evolutionary algorithm”, in IX International Scientific Conference “Intellectual Analysis of Information IAI-2009".
N. Draper and G. Smith, Applied regression analysis. Multiple regression, 3rd ed. Moscow: “Dialektika”, 2007, p. 912.