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Evolutionary Based Learning of Fuzzy Controllers

  • Chapter
Fuzzy Evolutionary Computation

Abstract

The term evolutionary computation usually refers to the design of adaptive systems using evolutionary principles. This term and others such as evolutionary algorithms [1] or evolutionary programs [2] have come to refer to the union of different families of methods (genetic algorithms [3], evolution strategies [4], evolutionary programming [6, 7]) proposed with this aim. The algorithms applied in evolutionary computation are population-based search methods that employ some kind of selection process to bias the search toward good solutions. Consequently, the idea of evolutionary based learning is that of a learning process where the main role in learning is carried out by evolutionary computation. The key principles of such a process are: to maintain a population of potential solutions for the problem to be solved, to design a set of evolution operators that search for new and/or better potential solutions and to define a suitable performance index to drive the section process.

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Authors and Affiliations

  1. Universidad Politecnica de Madrid, Madrid, 28040, Spain

    Luis Magdalena & Juan R. Velasco

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  1. Luis Magdalena
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  2. Juan R. Velasco
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Editor information

Editors and Affiliations

  1. The University of Manitoba, Winnipeg, Manitoba, Canada

    Witold Pedrycz

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© 1997 Springer Science+Business Media New York

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Magdalena, L., Velasco, J.R. (1997). Evolutionary Based Learning of Fuzzy Controllers. In: Pedrycz, W. (eds) Fuzzy Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6135-4_11

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  • DOI: https://doi.org/10.1007/978-1-4615-6135-4_11

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7811-2

  • Online ISBN: 978-1-4615-6135-4

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