Multi-Objective Evolutionary Optimization Algorithms for Machine Learning: A Recent Survey

Stamatios-Aggelos N. Alexandropoulos (), Christos K. Aridas (), Sotiris B. Kotsiantis () and Michael N. Vrahatis ()
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Stamatios-Aggelos N. Alexandropoulos: University of Patras
Christos K. Aridas: University of Patras
Sotiris B. Kotsiantis: University of Patras
Michael N. Vrahatis: University of Patras

A chapter in Approximation and Optimization, 2019, pp 35-55 from Springer

Abstract: Abstract The machine learning algorithms exploit a given dataset in order to build an efficient predictive or descriptive model. Multi-objective evolutionary optimization assists machine learning algorithms to optimize their hyper-parameters, usually under conflicting performance objectives and selects the best model for a given task. In this paper, recent multi-objective evolutionary approaches for four major data mining and machine learning tasks, namely: (a) data preprocessing, (b) classification, (c) clustering, and (d) association rules, are surveyed.

Date: 2019
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DOI: 10.1007/978-3-030-12767-1_4

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