Evolutionary Based Learning of Fuzzy Controllers

Luis Magdalena and Juan R. Velasco
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Luis Magdalena: Universidad Politecnica de Madrid
Juan R. Velasco: Universidad Politecnica de Madrid

Chapter 2.8 in Fuzzy Evolutionary Computation, 1997, pp 249-268 from Springer

Abstract: 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.

Keywords: Genetic Algorithm; Membership Function; Fuzzy System; Rule Base; Fuzzy Controller (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_11

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DOI: 10.1007/978-1-4615-6135-4_11

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