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Multiobjective Optimization Using Ideas from the Clonal Selection Principle

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Genetic and Evolutionary Computation — GECCO 2003 (GECCO 2003)

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Abstract

In this paper, we propose a new multiobjective optimization approach based on the clonal selection principle. Our approach is compared with respect to other evolutionary multiobjective optimization techniques that are representative of the state-of-the-art in the area. In our study, several test functions and metrics commonly adopted in evolutionary multiobjective optimization are used. Our results indicate that the use of an artificial immune system for multiobjective optimization is a viable alternative.

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Author information

Authors and Affiliations

  1. Evolutionary Computation Group, Depto. de Ingeniería Eléctrica, Sección de Computación, CINVESTAV-IPN, Av. Instituto Politécnico Nacional No. 2508 Col. San Pedro Zacatenco, México, D. F., 07300, Mexico

    Nareli Cruz Cortés & Carlos A. Coello Coello

Authors
  1. Nareli Cruz Cortés
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  2. Carlos A. Coello Coello
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Editor information

Editors and Affiliations

  1. Lawrence Livermore National Laboratory, Center for Applied Scientific Computing (CASC), 7000 East Avenue, L-561, Livermore, CA, 94550, USA

    Erick Cantú-Paz

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Cortés, N.C., Coello, C.A.C. (2003). Multiobjective Optimization Using Ideas from the Clonal Selection Principle. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_22

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  • DOI: https://doi.org/10.1007/3-540-45105-6_22

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40602-0

  • Online ISBN: 978-3-540-45105-1

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