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Multiple Criteria Optimization: State of the Art Annotated Bibliographic Surveys pp 277–331Cite as

Evolutionary Algorithms and Multiple Objective Optimization

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Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 52))

Abstract

This chapter presents a review of the most important evolutionary multiobjective optimization techniques developed to date. Using as a basis a simple taxonomy of approaches, we briefly describe and analyze the advantages and disadvantages of each of them, together with some of their applications reported in the literature. Other important issues such as diversity and some of the main techniques developed to preserve it, as well as the need of suitable test functions and metrics that can properly evaluate the performance of these multiobjective optimization techniques are also addressed. We conclude this chapter with a brief outline of some potential paths of future research in this area.

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  1. CINVESTAV-IPN, Departamento de Ingeniería Eléctrica, Av. Instituto Politécnico Nacional No. 2508, Col. San Pedro Zacatenco, México, D.F., 07300, Mexico

    Carlos A. Coello Coello

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    Carlos E. Mariano Romero

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Coello Coello, C.A., Mariano Romero, C.E. (2003). Evolutionary Algorithms and Multiple Objective Optimization. In: Ehrgott, M., Gandibleux, X. (eds) Multiple Criteria Optimization: State of the Art Annotated Bibliographic Surveys. International Series in Operations Research & Management Science, vol 52. Springer, Boston, MA. https://doi.org/10.1007/0-306-48107-3_6

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