Machine learning into metaheuristics: A survey and taxonomy of data-driven metaheuristics - Archive ouverte HAL Access content directly
Preprints, Working Papers, ... Year :

Machine learning into metaheuristics: A survey and taxonomy of data-driven metaheuristics

(1)
1

Abstract

During the last years, research in applying machine learning (ML) to design efficient, effective and robust metaheuristics became increasingly popular. Many of those data driven 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 paper we will investigate different opportunities for using ML into metaheuristics. We define uniformly the various ways synergies which might be achieved. A detailed taxonomy is proposed according to the concerned search component: target optimization problem, 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 which needs further in-depth investigations.
Fichier principal
Vignette du fichier
ACM-CR.pdf (705.28 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02745295 , version 1 (03-06-2020)

Identifiers

  • HAL Id : hal-02745295 , version 1

Cite

El-Ghazali Talbi. Machine learning into metaheuristics: A survey and taxonomy of data-driven metaheuristics. 2020. ⟨hal-02745295⟩
1348 View
4114 Download

Share

Gmail Facebook Twitter LinkedIn More