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Schemata-Driven Multi-objective Optimization

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Evolutionary Multi-Criterion Optimization (EMO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2632))

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Abstract

This paper investigates the exploitation of non-dominated sets’ schemata in guiding multi-objective optimization. Schemata capture the similarities between solutions in the non-dominated set. They also reflect the knowledge acquired by multi-objective evolutionary algorithms. A schemata-driven genetic algorithm as well as a schemata-driven local search algorithm are described. An experimental study to evaluate the suggested approach is then conducted.

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Author information

Authors and Affiliations

  1. ECE Department, Concordia University, 1455 De Maisonneuve Blvd. W., Montreal, PQ, Canada, H3G 1M8

    Skander Kort

Authors
  1. Skander Kort
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Editor information

Editors and Affiliations

  1. Faculty of Sciences and Technology, Centre for Intelligent Systems, University of the Algarve, Campus de Gambelas, 8005-139, Faro, Portugal

    Carlos M. Fonseca

  2. Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Peter J. Fleming

  3. Department of Electrical Engineering, Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, GloriastraĂźe 35, 8092, ZĂĽrich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

  4. Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, Kanpur, UP, 208 016, India

    Kalyanmoy Deb

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© 2003 Springer-Verlag Berlin Heidelberg

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Kort, S. (2003). Schemata-Driven Multi-objective Optimization. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_14

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  • DOI: https://doi.org/10.1007/3-540-36970-8_14

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-01869-8

  • Online ISBN: 978-3-540-36970-7

  • eBook Packages: Springer Book Archive

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