Benchmarking of Traditional Method of Genetic Algorithm with the Real Coded Method with a modified type of 2-Point Crossover (F-Crossover)
Konferenz: ISTET 2009 - VXV International Symposium on Theoretical Engineering
22.06.2009 - 24.06.2009 in Lübeck, Germany
Tagungsband: ISTET 2009
Seiten: 5Sprache: EnglischTyp: PDFPersönliche VDE-Mitglieder erhalten auf diesen Artikel 10% Rabatt
Khan, Umair Ali; Fasih, Alireza; Kyamakya, Kyandoghere; Chedjou, Jean Chamberlain (Transportation Informatics Group, Alpen Adria University, Klagenfurt, Austria)
A benchmarking using cellular neural networks is performed between the traditional method of Genetic algorithm (using binary population of random chromosomes) with a real coded approach of genetic algorithm. The benchmarking was done with various image processing operations and it is shown that in most of the image processing operations, real coded appraoch converges faster. Real numbers population prevents the repeated encoding and decoding of chromosomes. Also the sizes of chromosomes are relatively smaller. Moreover, a modified type of 2-point crossover (F-Crossover) is introduced which decreases the convergence time of the genetic algorithm and eliminates the need of mutation.