Learning variable importance to guide recombination on many-objective optimization

Abstract : There are numerous many-objective real-world problems in various application domains for which it is difficult or time-consuming to derive Pareto optimal solutions. In an evolutionary algorithm, variation operators such as recombination and mutation are extremely important to obtain an effective solution search. In this paper, we study a machine learning-enhanced recombination that incorporates an intelligent variable selection method. The method is based on the importance of variables with respect to convergence to the Pareto front. We verify the performance of the enhanced recombination on benchmark test problems with three or more objectives using the many-objective evolutionary algorithm AϵSϵH as a baseline algorithm. Results show that variable importance can enhance the performance of many-objective evolutionary algorithms.
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Communication dans un congrès
5th International Conference on Smart Computing and Artificial Intelligence (SCAI 2017), Jul 2017, Hamamatsu, Japan. 2017
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Soumis le : lundi 4 septembre 2017 - 14:39:41
Dernière modification le : jeudi 7 février 2019 - 17:29:28

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Miyako Sagawa, Hernán Aguirre, Fabio Daolio, Arnaud Liefooghe, Bilel Derbel, et al.. Learning variable importance to guide recombination on many-objective optimization. 5th International Conference on Smart Computing and Artificial Intelligence (SCAI 2017), Jul 2017, Hamamatsu, Japan. 2017. 〈hal-01581247〉

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