Meta-heuristic multi- and many-objective optimization techniques for solution of machine learning problems
Expert Systems. Hoboken: Wiley, v. 34, n. 6, 12 p., 2017.
Universidade Federal de São Carlos (UFSCar)
Universidade Estadual Paulista (UNESP)
Ohio State Univ
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.