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Crossing unordered sets of rules in evolutionary fuzzy controllers
Author(s) -
Magdalena Luis
Publication year - 1998
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/(sici)1098-111x(199810/11)13:10/11<993::aid-int7>3.0.co;2-u
Subject(s) - crossover , operator (biology) , computer science , base (topology) , set (abstract data type) , table (database) , decision table , fuzzy logic , fuzzy rule , data mining , artificial intelligence , genetic algorithm , fuzzy set , theoretical computer science , rough set , mathematics , machine learning , programming language , mathematical analysis , biochemistry , chemistry , repressor , transcription factor , gene
In recent years the use of genetic or evolutionary techniques has produced interesting results in the automatic generation of knowledge bases for fuzzy logic controllers. Three different representations of the rule base have been considered: lists of rules, relational matrices, and decision tables. The use of lists of rules reduces the size of the rule base, but presents some handicaps in crossover since it usually requires some kind of list ordering before applying the operator. A new crossover operator, working with lists (sets) of rules, is designed in such a way that maintaining the advantage of working with a reduced set of rules incorporates the characteristic of easy crossover by using the virtual structure of decision table. © 1998 John Wiley & Sons, Inc.