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World Wisepersons Workshop

WWW 1995: Fuzzy Logic, Neural Networks, and Evolutionary Computation pp 1–20Cite as

The design of hybrid fuzzy/evolutionary multiobjective optimization algorithms

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1152))

Abstract

In this paper, we develop and demonstrate techniques to design multiobjective optimization algorithms based on hybrid fuzzy system/evolutionary algorithm techniques. The technique is based on an approach where a fuzzy system is used to control the evolutionary algorithm. By viewing the search process as a dynamic process, high performance strategies are developed using controller design techniques. Through the use of indicators aimed at assessing the performance of evolutionary algorithms for multiobjective optimization, we show how to design fuzzy systems for controlling the search behavior. The key contributions of the work reported in this paper are the techniques for quantitatively measuring the performance of population based multiobjective optimization algorithms and techniques for automatically designing optimization algorithms. We demonstrate our techniques on an Integrated Circuit placement task that includes timing and geometrical objectives.

This work was initiated when the author was a Siemens Visiting Industrial Fellow at UC Berkeley. The author is now with Motorola in Geneve, Switzerland.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Sciences, University of California, 94720, Berkeley, CA, USA

    Michael A. Lee, Henrik Esbensen & Laurent Lemaitre

Authors
  1. Michael A. Lee
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  2. Henrik Esbensen
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  3. Laurent Lemaitre
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Editor information

Takeshi Furuhashi Yoshiki Uchikawa

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© 1996 Springer-Verlag Berlin Heidelberg

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Lee, M.A., Esbensen, H., Lemaitre, L. (1996). The design of hybrid fuzzy/evolutionary multiobjective optimization algorithms. In: Furuhashi, T., Uchikawa, Y. (eds) Fuzzy Logic, Neural Networks, and Evolutionary Computation. WWW 1995. Lecture Notes in Computer Science, vol 1152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61988-7_13

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  • DOI: https://doi.org/10.1007/3-540-61988-7_13

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61988-8

  • Online ISBN: 978-3-540-49581-9

  • eBook Packages: Springer Book Archive

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