CRGA--一种基于保留全局公共模式和约束交叉位置的遗传算法

提出了一种基于保留全局公共模式和约束交叉位置的遗传算法CRGA，该算法解决了标准交叉算子容易破坏高阶、长而好的模式及其在相似个体之间低效的问题，CRGA通过对适应度高于群体平均适应度的个体模式基因值的统计来估算父个体基因值在子个体中保留的概率，从而达到对高阶、长而好的模式的保护；同时通过约束交叉位置，保证了交叉操作一定能产生新个体．实验结果表明，CRGA算法在收敛精度和收敛速度上都要明显优于基于标准交叉算子的遗传算法．

CRGA-A Genetic Algorithm Based on Preserving Global Commonality Schemata and Restricting Local of Crossover

A new genetic algorithm based on preserving global commonality schemata and restricting local of crossover is proposed. This algorithm resolves the problem that is the two disadvantages of a standard crossover operator: disruption of high rank, long and good schemata and inefficiency between similar individuals. This algorithm protects the high rank, long and good schemata by estimating the probability of parent alleles preserved in son based on the statistic of all individuals whose fitness is better than average fitness of population, and ensures to produce new individual by restricting the local of crossover. The experiment result shows that the convergence precision and the convergence speed of this algorithm are evidently better than that of genetic algorithm based on standard crossover.