基于变异的紧凑遗传算法

Compact Genetic Algorithms Based on Mutation

紧凑遗传算法(CGA)具有存储成本低的优点,但是其容易出现早熟。该文提出一种基于变异的紧凑遗传算法(MBCGA)。MBCGA在CGA的基础上,引进变异算子,完整地体现生态进化中的选择、遗传和变异,提高了局部寻优以及算法克服早熟的能力。试验结果表明,MBCGA保留存储成本低的优点,具有较快的收敛速度。变异算子的局部寻优作用明显。

Compact Genetic Algorithm（CGA） requires a small amount of memory, but it is apt to premature stagnate. This paper proposes a Mutation-Based Compact Genetic Algorithm（MBCGA） by introducing the mutation operator into CGA, thus MBCGA mimics all the main genetic operators in natural evolution, then local search is strengthened and premature stagnation can be avoided. Experimental results show that the MBCGA generally exhibits a higher rate of convergence than CGA, without increasing the memory requirement. The effect of the introduced mutation operator is analyzed and verified.