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AutoMH: Automatically Create Evolutionary Metaheuristic Algorithms Using Reinforcement Learning [PDF]
Entropy, 2022 Machine learning research has been able to solve problems in multiple domains. Machine learning represents an open area of research for solving optimisation problems. The optimisation problems can be solved using a metaheuristic algorithm, which can find
Boris Almonacid
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How metaheuristic algorithms can help in feature selection for Alzheimer’s diagnosis [PDF]
International Journal of Research in Industrial Engineering, 2023 Feature selection is the process of picking the most effective feature among a considerable number of features in the dataset. However, choosing the best subset that gives a higher performance in classification is challenging.
Farzaneh Salami +2 more
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Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)
IEEE Access, 2021 Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem.
Prachi Agrawal +3 more
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Survey of Lévy Flight-Based Metaheuristics for Optimization
Mathematics, 2022 Lévy flight is a random walk mechanism which can make large jumps at local locations with a high probability. The probability density distribution of Lévy flight was characterized by sharp peaks, asymmetry, and trailing.
Juan Li +4 more
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An Overview of the Concepts, Classifications, and Methods of Population Initialization in Metaheuristic Algorithms [PDF]
Journal of Advances in Computer Engineering and Technology, 2021 Metaheuristic algorithms are typically population-based random search techniques. The general framework of a metaheuristic algorithm consisting of its main parts.
Mohammad Hassanzadeh, farshid keynia
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IEEE Access, 2022 Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling.
Osamah Mohammed Alyasiri +3 more
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Permutation Tests for Metaheuristic Algorithms
Mathematics, 2022 Many metaheuristic approaches are inherently stochastic. In order to compare such methods, statistical tests are needed. However, choosing an appropriate test is not trivial, given that each test has some assumptions about the distribution of the ...
Mahamed G. H. Omran +4 more
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KPLS Optimization With Nature-Inspired Metaheuristic Algorithms
IEEE Access, 2020 Kernel partial least squares regression (KPLS) is a technique used in several scientific areas because of its high predictive ability. This article proposes a methodology to simultaneously estimate both the parameters of the kernel function and the ...
Jorge Daniel Mello-Roman +1 more
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Energies, 2020 In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring.
Gianfranco Chicco, Andrea Mazza
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Buildings, 2022 This study proposes a generally applicable improvement strategy for metaheuristic algorithms, improving the algorithm’s accuracy and local convergence in finite element (FE) model updating.
Shiqiang Qin +3 more
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