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• W3159134414 endingPage "422" @default.
• W3159134414 startingPage "393" @default.
• W3159134414 abstract "In recent years, there has been a growing research interest in integrating machine learning techniques into meta-heuristics for solving combinatorial optimization problems. This integration aims to lead meta-heuristics toward an efficient, effective, and robust search and improve their performance in terms of solution quality, convergence rate, and robustness. Since various integration methods with different purposes have been developed, there is a need to review the recent advances in using machine learning techniques to improve meta-heuristics. To the best of our knowledge, the literature is deprived of having a comprehensive yet technical review. To fill this gap, this paper provides such a review on the use of machine learning techniques in the design of different elements of meta-heuristics for different purposes including algorithm selection, fitness evaluation, initialization, evolution, parameter setting, and cooperation. First, we describe the key concepts and preliminaries of each of these ways of integration. Then, the recent advances in each way of integration are reviewed and classified based on a proposed unified taxonomy. Finally, we provide a technical discussion on the advantages, limitations, requirements, and challenges of implementing each of these integration ways, followed by promising future research directions." @default.
• W3159134414 created "2021-05-10" @default.
• W3159134414 creator A5002914317 @default.
• W3159134414 creator A5019858282 @default.
• W3159134414 creator A5027757292 @default.
• W3159134414 creator A5030200712 @default.
• W3159134414 creator A5073929039 @default.
• W3159134414 date "2022-01-01" @default.
• W3159134414 modified "2023-10-14" @default.
• W3159134414 title "Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art" @default.
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