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A Review of Techniques for Handling Expensive Functions in Evolutionary Multi-Objective Optimization

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Computational Intelligence in Expensive Optimization Problems

Part of the book series: Adaptation Learning and Optimization ((ALO,volume 2))

Abstract

Evolutionary algorithms have been very popular for solving multiobjective optimization problems, mainly because of their ease of use, and their wide applicability. However, multi-objective evolutionary algorithms (MOEAs) tend to consume an important number of objective function evaluations, in order to achieve a reasonably good approximation of the Pareto front. This is a major concern when attempting to use MOEAs for real-world applications, since we can normally afford only a fairly limited number of fitness function evaluations in such cases. Despite these concerns, relatively few efforts have been reported in the literature to reduce the computational cost of MOEAs. It has been until relatively recently, that researchers have developed techniques to achieve an effective reduction of fitness function evaluations by exploiting knowledge acquired during the search. In this chapter, we analyze different proposals currently available in the specialized literature to deal with expensive functions in evolutionary multi-objective optimization. Additionally, we review some real-world applications of these methods, which can be seen as case studies in which such techniques led to a substantial reduction in the computational cost of the MOEA adopted. Finally, we also indicate some of the potential paths for future research in this area.

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  1. CINVESTAV-IPN (Evolutionary Computation Group), Departamento de Computación, Av. IPN No. 2508, Col. San Pedro Zacatenco, México, D.F., 07360, Mexico

    Luis V. Santana-Quintero, Alfredo Arias Montaño & Carlos A. Coello Coello

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  1. Department of Mechanical Engineering and Science-Faculty of Engineering, Kyoto University, Yoshida-honmachi, Sakyo-Ku,, 606-8501, Kyoto, Japan

    Yoel Tenne

  2. Advanced Technology Centre, Rolls-Royce Singapore Pte Ltd, 50 Nanyang Avenue, Block N2, Level B3C,Unit 05-08, 639798, Singapore

    Chi-Keong Goh

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Santana-Quintero, L.V., Montaño, A.A., Coello, C.A.C. (2010). A Review of Techniques for Handling Expensive Functions in Evolutionary Multi-Objective Optimization. In: Tenne, Y., Goh, CK. (eds) Computational Intelligence in Expensive Optimization Problems. Adaptation Learning and Optimization, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10701-6_2

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