一种改进的遗传算法

传统的遗传算法有两个严重的缺点,即不能有效地克服过早收敛现象,以及在进化后期搜索效率较低。模拟退火算法是基于金属退火的机理而建立起来的一种全局最优化方法,它能够以随机搜索技术从概率的意义上找到目标函数的全局最小点。本文将遗传算法与模拟退火算法相结合,提出模拟退火遗传算法,实验结果表明,该算法在性能上有较大的提高。

Performance Appraisement of the Simulated Annealing Genetic Algorithms

Traditional Genetic algorithm have two serious shortcomings,namely can't overcame and restrained the phenomenon for a long time effectively,And is evolving on later stage and searching for efficiency relatively low. Simulation anneal algorithm to set up a kind of the overall situation that stand up optimize the method most on the basis of mechanism that the metal anneals, It can be in order to search for small spot the most of the overall situation that technology finds the function of targets from meaning of probability at random. This text anneal Genetic algorithm and simulation algorithm combine together,propose the simulated annealing Genetic algorithm. The experimental result shows, can their is greater improvement on performance in this algorithm.