Influence of the Migration Process on the Learning Performances of Fuzzy Knowledge Bases

Author

Akrout, Khaled ; Baron, Luc ; Balazinski, Marek ; Achiche, Sofiane

Author_Institution

Ecole Polytech. of Montreal, Montreal

fYear

2007

fDate

24-27 June 2007

Firstpage

478

Lastpage

483

Abstract

This paper presents the influence of the process of migration between populations in GENO-FLOU, which is an environment of learning of fuzzy knowledge bases by genetic algorithms. Initially the algorithm did not use the process of migration. For the learning, the algorithm uses a hybrid coding, binary for the base of rules and real for the data base. This hybrid coding used with a set of specialized operators of reproduction proven to be an effective environment of learning. Simulations were made in this environment by adding a process of migration. While varying the number of populations, the number of generations and the rate of migration or simply the migration of the best elements, on various types of problems. In general, simulations show a significant improvement of the results obtained with migration. The variation of these parameters makes it possible to conclude on the dominating importance of the number of migrant generations.

Keywords

decision making; fuzzy logic; genetic algorithms; knowledge based systems; learning (artificial intelligence); GENO-FLOU; fuzzy knowledge bases; genetic algorithms; hybrid coding; learning performances; migration process; Clustering algorithms; Data mining; Data preprocessing; Decision making; Fuzzy logic; Fuzzy sets; Fuzzy systems; Genetic algorithms; Mechanical engineering; Performance analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 2007. NAFIPS '07. Annual Meeting of the North American

Conference_Location

San Diego, CA

Print_ISBN

1-4244-1213-7

Electronic_ISBN

1-4244-1214-5

Type

conf

DOI

10.1109/NAFIPS.2007.383887

Filename

4271110