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A mapping technique for better solution exploration: NSGA-II adaptation

Abstract : A mapping method (MaM) for a better solution space exploration adapted to NSGA-II method is presented. The Mapping technique divides the solution space into several zones using a Hamming distance to a reference solution. We present a bijective mapping function from the search space to the binary representation space of solutions. For each zone, a mapping metric is used to evaluate the solution space exploration. According to this evaluation, a local search is performed. The mapping is adapted to the well known non-dominated sorting genetic algorithm-II (NSGA-II) method applied to solve the flexible job shop problem (FJSP) case. We present the comparison between the hybridization using the local search for the non-dominated solutions and the hybridization using the mapping metrics. The multi-objective metrics show the efficiency of mapping adaptation in terms of convergence and diversity.
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Contributor : Daniel Gavrysiak Connect in order to contact the contributor
Submitted on : Thursday, February 13, 2020 - 11:37:35 AM
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Julien Autuori, Faicel Hnaien, Farouk Yalaoui. A mapping technique for better solution exploration: NSGA-II adaptation. Journal of Heuristics, Springer Verlag, 2016, 22 (1), pp.89-123. ⟨10.1007/s10732-015-9303-4⟩. ⟨hal-02477225⟩



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