Real/Binary-Like Coded Genetic Algorithm to Automatically Generate Fuzzy Knowledge Bases

Sofiane Achiche, Luc Baron, Marek Balazinski

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

This paper presents the results of the implementation of a combination of a real-coded and binary-like coded genetic algorithm (RBLGA) to automatically generate fuzzy knowledge bases (FKB) from a set of numerical data. The algorithm allows one to fulfil a contradictory paradigm in term of FKB precision and simplicity (high precision generally translates into high complexity level) considering a randomly generated population of potential FKBs. The RBLGA is divided in two principal coding ways, on one hand a real coded genetic algorithm (RCGA) that maps the fuzzy sets repartition and number (which drives the number of fuzzy rules) into a set of real numbers, on the other hand a binary like genetic algorithm deals with the fuzzy rule base (a set of integer numbers is used). The RBLGA uses three reproduction mechanisms, a BLX-alpha, a simple crossover and a fuzzy set reducer. The RBLGA is validated through a theoretical surface and, finally, applied to a set of experimental data.
Original languageEnglish
Title of host publicationIEEE Fourth International Conference on Control and Automation
Publication date2003
Pages799-803
Publication statusPublished - 2003
Externally publishedYes
EventIEEE Fourth International Conference on Control and Automation - Montreal, Quebec, Canada
Duration: 1 Jan 2003 → …

Conference

ConferenceIEEE Fourth International Conference on Control and Automation
CityMontreal, Quebec, Canada
Period01/01/2003 → …

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