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Meta‐heuristic multi‐ and many‐objective optimization techniques for solution of machine learning problems
Author(s) -
Rodrigues Douglas,
Papa João P.,
Adeli Hojjat
Publication year - 2017
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12255
Subject(s) - computer science , machine learning , artificial intelligence , meta learning (computer science) , cluster analysis , hyper heuristic , heuristic , context (archaeology) , selection (genetic algorithm) , fitness function , meta heuristic , unsupervised learning , feature selection , genetic algorithm , robot learning , task (project management) , algorithm , paleontology , management , mobile robot , robot , economics , biology
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.