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Improving Evolutionary Multi-objective Optimization Using Genders

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Artificial Intelligence and Soft Computing – ICAISC 2006 (ICAISC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4029))

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Abstract

In solving highly dimensional multi-objective optimization (EMO) problems by evolutionary computations the concept of Pareto-domination appears to be not effective. The paper discusses a new approach to EMO by introducing a concept of genetic genders for the purpose of making distinction between different groups of objectives. This approach is also able to keep diversity among the Pareto-optimal solutions produced.

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Author information

Authors and Affiliations

  1. Faculty of Electronics, Telecommunication and Computer Science, Gdansk University of Technology, 80-952, Gdansk, Poland

    Zdzislaw Kowalczuk & Tomasz Bialaszewski

Authors
  1. Zdzislaw Kowalczuk
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  2. Tomasz Bialaszewski
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Berkeley Initiative in Soft Computing (BISC), University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Żurada

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© 2006 Springer-Verlag Berlin Heidelberg

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Kowalczuk, Z., Bialaszewski, T. (2006). Improving Evolutionary Multi-objective Optimization Using Genders. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_42

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  • DOI: https://doi.org/10.1007/11785231_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35748-3

  • Online ISBN: 978-3-540-35750-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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