Many-Objective Problems: Challenges and Methods View Full Text


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Chapter Info

DATE

2015

AUTHORS

Antonio López Jaimes , Carlos A. Coello Coello

ABSTRACT

This chapter presents a short review of the state-of-the-art efforts for understanding and solving problems with a large number of objectives (usually known as many-objective optimization problems, M OP s ). The first part of the chapter presents the current studies aimed at discovering the sources that make a multiobjective optimization problem (MOP) harder when more objectives are added, degrading in this way, the performance of a multiobjective evolutionary algorithm (MOEA ). Next, some of the most relevant techniques designed to deal with M OPs are presented and categorized. More... »

PAGES

1033-1046

References to SciGraph publications

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  • Book

    TITLE

    Springer Handbook of Computational Intelligence

    ISBN

    978-3-662-43504-5
    978-3-662-43505-2

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-662-43505-2_51

    DOI

    http://dx.doi.org/10.1007/978-3-662-43505-2_51

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1048090192


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