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Multi-objective Evolutionary Algorithms: Introducing Bias Among Pareto-optimal Solutions

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Advances in Evolutionary Computing

Part of the book series: Natural Computing Series ((NCS))

Abstract

Since the beginning of the 1990s, research and application of multiobjective evolutionary algorithms (MOEAs) have attracted increasing attention. This is mainly due to the ability of evolutionary algorithms to find multiple Paretooptimal solutions in one single simulation run. In this chapter, we present an overview of MOEAs and then discuss a particular algorithm in detail. Although MOEAs can find multiple Pareto-optimal solutions, often, users need to impose a particular order of priority to objectives. In this chapter, we present a few classical techniques to identify a preferred or a compromise solution, and finally suggest a biased sharing technique which can be used during the optimization phase to find a biased distribution of Pareto-optimal solutions in the region of interest. The results are encouraging and suggest further application of the proposed strategy to more complex multi-objective optimization problems.

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Author information

Authors and Affiliations

  1. Kanpur Genetic Algorithms Laboratory (KanGAL), Department of Mechanical Engineering, Indian Institute of Technology Kanpur, PIN 208 016, India

    Kalyanmoy Deb

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  1. Kalyanmoy Deb
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Editors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata, 700 035, India

    Ashish Ghosh

  2. Department of Management Information, Hannan University, 5-4-33 Amamhigigashi, Matsubara, Osaka, 580-8502, Japan

    Shigeyoshi Tsutsui

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© 2003 Springer-Verlag Berlin Heidelberg

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Deb, K. (2003). Multi-objective Evolutionary Algorithms: Introducing Bias Among Pareto-optimal Solutions. In: Ghosh, A., Tsutsui, S. (eds) Advances in Evolutionary Computing. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18965-4_10

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  • DOI: https://doi.org/10.1007/978-3-642-18965-4_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-62386-8

  • Online ISBN: 978-3-642-18965-4

  • eBook Packages: Springer Book Archive

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