Title :
A Self-Organization Genetic Algorithm with Cycle Mutation
Author :
Wang, Na ; Zhuang, Jian ; Du, Haifeng ; Wang, Sun An
Author_Institution :
Sch. of Mech. Eng., Xi´´an Jiaotong Univ., Xi´´an
Abstract :
In this paper, a mutation with cycle probability is designed by simulating the evolutionary rule of the earth creature, and a genetic algorithm based on the cycle mutation, presents the ability in improving search efficiency and overcoming premature to some extent. To further improve performance of the algorithm, the selection is mended according to the phenomena that optimum individual always plays a major role, and an improved cycle mutation genetic algorithm is proposed. The experiment results on the benchmark functions optimization show that exploration and exploitation of this algorithm is better than some well-known evolution algorithms and it is not sensitive to the initial population distribution.
Keywords :
genetic algorithms; probability; search problems; cycle mutation probability; earth creature; evolutionary rule; search problem; self-organization genetic algorithm; Artificial intelligence; Computational efficiency; Evolution (biology); Genetic algorithms; Genetic mutations; Mechanical engineering; Public policy; Scheduling algorithm; Size control; Testing;
Conference_Titel :
Tools with Artificial Intelligence, 2008. ICTAI '08. 20th IEEE International Conference on
Conference_Location :
Dayton, OH
Print_ISBN :
978-0-7695-3440-4
DOI :
10.1109/ICTAI.2008.30