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Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases pp 23–46Cite as

Knowledge Incorporation in Multi-objective Evolutionary Algorithms

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 98))

This chapter presents a survey of techniques used to incorporate knowledge into evolutionary algorithms, with a particular emphasis on multi-objective optimization. We focus on two main groups of techniques: those that incorporate knowledge into the fitness evaluation, and those that incorporate knowledge in the initialization process and the operators of an evolutionary algorithm. Several techniques representative of each of these groups are briefly discussed, together with some examples found in the specialized literature. In the last part of the chapter, we provide some research ideas that are worth exploring in the future by researchers interested in this topic.

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Authors and Affiliations

  1. Departamento de Computación, CINVESTAV-IPN, 07360, Mexico

    Ricardo Landa-Becerra, Luis V. Santana-Quintero & Carlos A. Coello Coello

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  1. Ricardo Landa-Becerra
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  2. Luis V. Santana-Quintero
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  3. Carlos A. Coello Coello
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Editors and Affiliations

  1. Indian Statistical Institute, 203 B. T. Road, 700 108, Kolkata, India

    Ashish Ghosh

  2. F. M. University, 756 019, Balasore, India

    Satchidananda Dehuri

  3. Jadavpur University, 700 032, Kolkata, India

    Susmita Ghosh

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Landa-Becerra, R., Santana-Quintero, L.V., Coello, C.A.C. (2008). Knowledge Incorporation in Multi-objective Evolutionary Algorithms. In: Ghosh, A., Dehuri, S., Ghosh, S. (eds) Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases. Studies in Computational Intelligence, vol 98. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77467-9_2

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  • DOI: https://doi.org/10.1007/978-3-540-77467-9_2

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  • Print ISBN: 978-3-540-77466-2

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