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Machine Learning into Metaheuristics

El-Ghazali Talbi 1, 2, 3 
1 BONUS - Optimisation de grande taille et calcul large échelle
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : During the past few years, research in applying machine learning (ML) to design efficient, effective, and robust metaheuristics has become increasingly popular. Many of those machine learning-supported metaheuristics have generated high-quality results and represent state-of-the-art optimization algorithms. Although various appproaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this research topic. In this article, we will investigate different opportunities for using ML into metaheuristics. We define uniformly the various ways synergies that might be achieved. A detailed taxonomy is proposed according to the concerned search component: target optimization problem and low-level and high-level components of metaheuristics. Our goal is also to motivate researchers in optimization to include ideas from ML into metaheuristics. We identify some open research issues in this topic that need further in-depth investigations.
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https://hal.inria.fr/hal-03339904
Contributor : TALBI El-Ghazali Connect in order to contact the contributor
Submitted on : Thursday, September 9, 2021 - 5:22:36 PM
Last modification on : Friday, July 8, 2022 - 10:09:41 AM

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El-Ghazali Talbi. Machine Learning into Metaheuristics. ACM Computing Surveys, Association for Computing Machinery, 2021, 54 (6), pp.1-32. ⟨10.1145/3459664⟩. ⟨hal-03339904⟩

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