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Evolutionary Based Learning of Fuzzy Controllers

In: Fuzzy Evolutionary Computation

Author

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  • Luis Magdalena

    (Universidad Politecnica de Madrid)

  • Juan R. Velasco

    (Universidad Politecnica de Madrid)

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.

Suggested Citation

  • Luis Magdalena & Juan R. Velasco, 1997. "Evolutionary Based Learning of Fuzzy Controllers," Springer Books, in: Witold Pedrycz (ed.), Fuzzy Evolutionary Computation, chapter 3, pages 249-268, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4615-6135-4_11
    DOI: 10.1007/978-1-4615-6135-4_11
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