DocumentCode :
468437
Title :
A Dynamic Fuzzy-Based Crossover Method for Genetic Algorithms
Author :
Amraii, S. Amirpour ; Ajallooeian, M. ; Lucas, C.
Author_Institution :
Univ. of Tehran, Tehran
Volume :
1
fYear :
2007
fDate :
29-31 Oct. 2007
Firstpage :
465
Lastpage :
471
Abstract :
Currently, genetic algorithms (GA) are widely used in different optimization problems. One of the problems with GAs is tuning their parameters correctly as they can have a significant effect on GA´s overall performance. Till now, different methods have been proposed for fine tuning these parameters. Many of these methods use fuzzy linguistic rules in order to find the correct parameters in each stage of the GA evolution. But these methods look at each chromosome as a whole solution for a specific problem. In our contribution, a new method has been proposed which breaks each chromosome into sub-parts and uses the better sub-solutions as the building blocks of the next generation using a fuzzy-based approach. The performance of this algorithm has been shown on the traveling salesman problem (TSP) with comparison to simple GA and adaptive GA.
Keywords :
fuzzy set theory; genetic algorithms; adaptive GA; dynamic fuzzy-based crossover method; fuzzy linguistic rules; genetic algorithms; optimization problems; traveling salesman problem; Artificial intelligence; Biological cells; Fuzzy logic; Genetic algorithms; Genetic mutations; Knowledge representation; Optimization methods; Traveling salesman problems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference on
Conference_Location :
Patras
ISSN :
1082-3409
Print_ISBN :
978-0-7695-3015-4
Type :
conf
DOI :
10.1109/ICTAI.2007.134
Filename :
4410321
Link To Document :
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