About |
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This special issue aims to provide a collection of high-quality research articles that address broad challenges in both theoretical and application aspects of the synergistic use of metaheuristics and machine learning. We invite colleagues to contribute original research articles as well as review articles that will stimulate the continuing efforts on the combination of metaheuristic schemes and machine learning techniques. In the special issue, the contributions are mainly divided into two groups: works where machine learning is employed to enhance metaheuristics, and those in which metaheuristics are used to improve the performance of machine learning techniques. |
Call for Papers |
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Potential topics include, but are not limited to: In the case of Machine learning techniques to enhance metaheuristics. Machine learning techniques such as Gaussian models, Bayesian inference, kernels, data association, Clustering, etc., for tuning metaheuristic approaches, as search mechanisms, for modifying the search structure, for selecting a certain metaheuristic for a particular problem, etc. The approaches are applied to single objective metaheuristic methods, multi-objective approaches, memetic techniques or hyper-heuristics. In the case of metaheuristics schemes to improve the performance of machine learning techniques. They include metaheuristic methods for classification, regression, clustering, rule mining, data association, etc. |
Credits and Sources |
| [1] META-MACHINE-SYNERGISTIC 2020 : Metaheuristic schemes and Machine learning techniques: A synergistic perspective |
