Design of metaheuristic based on machine learning: A unified approach

Amir Nakib 1 Mohamed Hilia 1 Frédéric Héliodore 2 El-Ghazali Talbi 3
3 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : In this work, a framework based on maximum likelihood estimation and mutual information is proposed to design a metaheuristic. A multilevel decomposition of metaheuristics is proposed that allow to have a unified vision on this optimization approach. Then, a new layer based on machine learning is added to take profit from the evolution of the algorithm to adapt it to the considered problem to alleviate users efforts interested in designing and implementing metaheuristics. In other terms, the goal is to proved an alternative to implement metaheuristics. Surprisingly, when results were compared to those of other classical metaheuristics, in most of cases the proposed approach provided good results.
Type de document :
Communication dans un congrès
IPDPS 2017 - International Parallel and Distributed Processing Symposium : Workshops, May 2017, Orlando, United States. IEEE, Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2017 IEEE International, pp.510-518, 2017, 〈10.1109/IPDPSW.2017.137〉
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https://hal.inria.fr/hal-01654860
Contributeur : Talbi El-Ghazali <>
Soumis le : lundi 4 décembre 2017 - 14:34:10
Dernière modification le : mercredi 30 mai 2018 - 01:20:35

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Amir Nakib, Mohamed Hilia, Frédéric Héliodore, El-Ghazali Talbi. Design of metaheuristic based on machine learning: A unified approach. IPDPS 2017 - International Parallel and Distributed Processing Symposium : Workshops, May 2017, Orlando, United States. IEEE, Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2017 IEEE International, pp.510-518, 2017, 〈10.1109/IPDPSW.2017.137〉. 〈hal-01654860〉

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