Title :
A hybrid strategy: real-coded genetic algorithm and chaotic search
Author :
Zhang, Chun-kai ; Shao, Hui-he
Author_Institution :
Inst. of Autom., Shanghai Jiao Tong Univ., China
Abstract :
A novel real-coded GA is proposed which utilizes chaotic search and variable evolutionary rate. By introducing these techniques, the GA algorithm can better simulate the process of biologic evolution, and possesses better hill-climbing ability. In addition, "family competition" is added into the process of mutation and the operating order of mutation and crossover operators is adaptively changed in a different evolutionary stage. Compared with self-adaptive GAs, the real-coded GA can overcome the shortcoming of premature convergence and stagnation, and effectively solves the problem of global convergence
Keywords :
adaptive systems; chaos; genetic algorithms; search problems; biologic evolution; chaotic search; crossover operators; evolutionary stage; family competition; global convergence; hill-climbing ability; hybrid strategy; mutation; operating order; premature convergence; real-coded GAs; real-coded genetic algorithm; self-adaptive GAs; stagnation; variable evolutionary rate; Automation; Biological system modeling; Biological systems; Chaos; Convergence; Evolution (biology); Genetic algorithms; Genetic mutations; Stochastic processes; Systems biology;
Conference_Titel :
Systems, Man, and Cybernetics, 2001 IEEE International Conference on
Conference_Location :
Tucson, AZ
Print_ISBN :
0-7803-7087-2
DOI :
10.1109/ICSMC.2001.972910