Fuzzy/Multiobjective Genetic Systems for Intelligent Systems Design Tools and Components

Michael A. Lee and Henrik Esbensen
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Michael A. Lee: University of California, Department of Electrical Engineering and Computer Sciences
Henrik Esbensen: Avanti Corporation

Chapter 1.3 in Fuzzy Evolutionary Computation, 1997, pp 57-78 from Springer

Abstract: Abstract In this chapter, we develop robust and efficient fuzzy/genetic based design tools and components for intelligent systems. The hybrid techniques exploit the knowledge representation capabilities of fuzzy systems and the adaptive capabilities of genetic algorithms. The core of the techniques presented in this chapter is a mutiobjective variation of genetic algorithms. We first demonstrate how the multiobjective genetic algorithm can be applied to fuzzy system design and then propose techniques to enhance the genetic algorithm technique using fuzzy systems. The fuzzy genetic system techniques proposed in this chapter provide intelligent systems designers with approaches to simultaneously perform structural and parameter identification of fuzzy systems and to carry out efficient genetic algorithm - based multiobjective optimization.

Keywords: Genetic Algorithm; Membership Function; Fuzzy System; Multiobjective Optimization; Multiobjective Evolutionary Algorithm (search for similar items in EconPapers)
Date: 1997
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4615-6135-4_3

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DOI: 10.1007/978-1-4615-6135-4_3

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