Title :
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
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;
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
DOI :
10.1109/NAFIPS.2007.383887
