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Considering that the performance of a genetic algorithm (GA) is affected by many factors and their rela-tionships are complex and hard to be described,a novel fuzzy-based adaptive genetic algorithm (FAGA) combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex problems.In FAGA,immune theory is used to improve the performance of selection operation.And,crossover probability and mutation probability are adjusted dynamically by fuzzy inferences,which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters.The experi-ments show that FAGA can efficiently overcome shortcomings of GA,i.e.,premature and slow,and obtain better results than two typical fuzzy GAs.Finally,FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.
Considering that the performance of a genetic algorithm (GA) is affected by many factors and their rela- tionships are complex and hard to be described, a novel fuzzy-based adaptive genetic algorithm (FAGA) combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex problems. In FAGA, immune theory is used to improve the performance of selection operation. And, crossover probability and mutation probability are adjusted dynamically by fuzzy inferences, which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters. The experi-ments show that FAGA could be overcome for shortcomings of GA, ie, premature and slow, and obtain better results than two typical fuzzy GAs. Finally, FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.