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

In: Approximation and Optimization

Author

Listed:
  • 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)

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.

Suggested Citation

  • Stamatios-Aggelos N. Alexandropoulos & Christos K. Aridas & Sotiris B. Kotsiantis & Michael N. Vrahatis, 2019. "Multi-Objective Evolutionary Optimization Algorithms for Machine Learning: A Recent Survey," Springer Optimization and Its Applications, in: Ioannis C. Demetriou & Panos M. Pardalos (ed.), Approximation and Optimization, pages 35-55, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-12767-1_4
    DOI: 10.1007/978-3-030-12767-1_4
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    Cited by:

    1. Suyun Liu & Luis Nunes Vicente, 2022. "Accuracy and fairness trade-offs in machine learning: a stochastic multi-objective approach," Computational Management Science, Springer, vol. 19(3), pages 513-537, July.

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