Dual population genetic algorithm with chaotic local search strategy
Xuedong Wu · Jisuanji yingyong yanjiu · 2011
This paper proposed dual population genetic algorithm with chaotic local search strategy(CLSDPGA) to improve local and global search ability of genetic algorithm.In CLSDPGA,one population was used as exploration population,the other was exploitation population.The two population was evolved by different crossover probability and mutation probability.At the end of each generation,applied chaotic local search to the optimal solution of each population,and the solution would be the new optimal solution if a solution found by chaotic local search was better than the optimal solution.Chaotic local search was not stopped until the predefined search time was elapsed.An immigration operation was down between the two population each ten generation.Experiment results on six benchmark functions show that CLSDPGA has the better ability of finding optimal solution than that of genetic algorithm with adaptive local search scheme(a-hGA2).