Title :
A Study of Compulsive Genetic Algorithm and Its Performance
Author :
Lei Pan ; Liangxian Gu ; Yuan Gao
Author_Institution :
Coll. of Astronaut., Northwestern Poly-Tech. Univ., Xian
Abstract :
The convergence and local research ability of genetic algorithm is a well concerned research field in recent years. New evolution law of species is introduced in the paper, and based on the new evolution law, compulsive operator was introduced and a new genetic algorithm - compulsive genetic algorithm (CGA) was proposed to improve the convergence of GA. CGA takes advantage of the fitness of current and past generations to create an approximation of the evolution process, identify the evolution direction and improve their evolution progress, which will accelerate the convergence of GA. Two experimental examples were computed to test the convergence and local research ability of CGA. The experimental results show that CGA is of good convergences and good local research ability.
Keywords :
approximation theory; convergence of numerical methods; genetic algorithms; mathematical operators; compulsive genetic algorithm; compulsive operator; convergence ability; evolution process approximation; local research ability; Acceleration; Automation; Convergence; DC generators; Educational institutions; Evolution (biology); Genetic algorithms; Life estimation; Optimization methods; Space technology; Compulse GA; Convergence; Genetic Algorithm;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2008 International Conference on
Conference_Location :
Hunan
Print_ISBN :
978-0-7695-3357-5
DOI :
10.1109/ICICTA.2008.200