Meta‐heuristic multi‐ and many‐objective optimization techniques for solution of machine learning problems |
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| Authors: | Douglas Rodrigues João P Papa Hojjat Adeli |
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| Affiliation: | 1. Department of Computing, Federal University of S?o Carlos, S?o Carlos, SP, Brazil;2. Department of Computing, S?o Paulo State University, Bauru, SP, Brazil;3. Departments of Civil, Environmental, and Geodetic Engineering, Biomedical Engineering, Neuroscience, The Ohio State University, Columbus, OH, USA |
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| Abstract: | Recently, multi‐ and many‐objective meta‐heuristic algorithms have received considerable attention due to their capability to solve optimization problems that require more than one fitness function. This paper presents a comprehensive study of these techniques applied in the context of machine learning problems. Three different topics are reviewed in this work: (a) feature extraction and selection, (b) hyper‐parameter optimization and model selection in the context of supervised learning, and (c) clustering or unsupervised learning. The survey also highlights future research towards related areas. |
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| Keywords: | machine learning meta‐heuristic algorithms multi‐objective optimization |
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