Title :
A Novel Fast Convergent Genetic Algorithms using Adaptive Techniques
Author_Institution :
Sch. of Sci., Hangzhou Dianzi Univ., Zhejiang
Abstract :
This paper presents modified genetic algorithms, which based on tuning of mutation probability by the value of individual fitness. The fine modular in current generation is easy to survive in the offspring, and at the same time the variety of population is also guaranteed. In modified scheme, the order of crossover and mutation is changed in order to avoid repeated computing of individual fitness. Simulation result shows that the modified scheme is prior to the GAs commonly used
Keywords :
genetic algorithms; probability; adaptive technique; convergent genetic algorithm; mutation probability; Algebra; Algorithm design and analysis; Computational modeling; Control systems; Cybernetics; Genetic algorithms; Genetic mutations; Least squares methods; Machine learning; Optimization methods; Robustness; System identification; Turning; Adaptive; Genetic Algorithms; Mutation; Probability;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258505