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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2007: Evolutionary Multi-Criterion Optimization pp 937–951Cite as

Overview of Artificial Immune Systems for Multi-objective Optimization

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 4403)

Abstract

Evolutionary algorithms have become a very popular approach for multiobjective optimization in many fields of engineering. Due to the outstanding performance of such techniques, new approaches are constantly been developed and tested to improve convergence, tackle new problems, and reduce computational cost. Recently, a new class of algorithms, based on ideas from the immune system, have begun to emerge as problem solvers in the evolutionary multiobjective optimization field. Although all these immune algorithms present unique, individual characteristics, there are some trends and common characteristics that, if explored, can lead to a better understanding of the mechanisms governing the behavior of these techniques. In this paper we propose a common framework for the description and analysis of multiobjective immune algorithms.

Keywords

  • Pareto Front
  • Multiobjective Optimization
  • Nondominated Solution
  • Immune Network
  • Immune Algorithm

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Hokkaido University, Laboratory of Hybrid Systems, Graduate School of Information Science and Technology, Sapporo, Japan

    Felipe Campelo & Hajime Igarashi

  2. Universidade Federal de Minas Gerais, Departamento de Engenharia Elétrica, Belo Horizonte, MG, Brazil

    Frederico G. Guimarães

Authors
  1. Felipe Campelo
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  2. Frederico G. Guimarães
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  3. Hajime Igarashi
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Shigeru Obayashi Kalyanmoy Deb Carlo Poloni Tomoyuki Hiroyasu Tadahiko Murata

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Campelo, F., Guimarães, F.G., Igarashi, H. (2007). Overview of Artificial Immune Systems for Multi-objective Optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds) Evolutionary Multi-Criterion Optimization. EMO 2007. Lecture Notes in Computer Science, vol 4403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70928-2_69

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  • DOI: https://doi.org/10.1007/978-3-540-70928-2_69

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70927-5

  • Online ISBN: 978-3-540-70928-2

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