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Evolution’s Niche in Multi-Criterion Problem Solving

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Biologically-Inspired Optimisation Methods

Part of the book series: Studies in Computational Intelligence ((SCI,volume 210))

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Abstract

In a short span of about 15 years, evolutionary multi-objective optimization (EMO) has progressed on a fast track in proposing, implementing, and applying efficient methodologies based on nature-inspired computational algorithms for optimization. In this chapter, we briefly describe the original motivation for developing EMO algorithms and provide an account of some successful problem domains on which EMO has demonstrated a clear edge over their classical counterparts. More success studies exist and many more problem areas are needed to be explored. Hopefully, this chapter provides an indication and flavor of some such problem domains which may get benefited from a systematic application of an EMO procedure.

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Authors and Affiliations

  1. Department of Business Technology, Helsinki School of Economics, Runeberginkatu 22-24, FIN-00101, Helsinki, Finland

    Kalyanmoy Deb

  2. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, PIN 208016, India

    Kalyanmoy Deb

Authors
  1. Kalyanmoy Deb
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Editor information

Editors and Affiliations

  1. Institute for Integrated and Intelligent Systems, Griffith University, Nathan Campus, 4111, Brisbane, Queensland, Australia

    Andrew Lewis

  2. Institut für Angewandte Informatik und Formale Beschreibungsverfahren - AIFB, Universität Karlsruhe, 76128, Karlsruhe, Germany

    Sanaz Mostaghim

  3. Faculty of Business, Technology and Sustainable Development, Bond University, 4229, Gold Coast, Queensland, Australia

    Marcus Randall

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© 2009 Springer-Verlag Berlin Heidelberg

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Deb, K. (2009). Evolution’s Niche in Multi-Criterion Problem Solving. In: Lewis, A., Mostaghim, S., Randall, M. (eds) Biologically-Inspired Optimisation Methods. Studies in Computational Intelligence, vol 210. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01262-4_1

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  • DOI: https://doi.org/10.1007/978-3-642-01262-4_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01261-7

  • Online ISBN: 978-3-642-01262-4

  • eBook Packages: EngineeringEngineering (R0)

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