遗传算法的改进

遗传算法是建立在遗传学与自然选择基础上的自适应索过程，作为解决复杂问题的一种有效手段，遗传算法是目前人工智能和系统优化领域的热点研究课题。但是，在实际应用中，简单跗算法存在收剑速度慢和稳定性差等缺陷。为克服这些问题，在对遗传算法的基本要点进行介绍的基础上，对交换、突变和复制等算子以及操作过程进行了改进。为了验证改进遗传算法的可行性与有效性，进行了多峰值函数的优化。试验结果表明，改进遗传３算法提高了

Improvement of Genetic Algorithm

Genetic algorithm, based on the mechanism ofgenetics and natural selection, is an adaptive searching procedure. As a powerful approachapplied to complex problems, genetic algorithm is a hot point for research on the fieldsof artificial intelligence and system optimization. However, simple genetic algorithm hasdrawbacks such as slow convergence and less stability in actual uses. To overcome theseproblems, crossover, mutation and reproduction operators as well as the procedure areimproved based on introduction of the principles. Multimodal function optimization isperformed to verify the feasibility and effectiveness. The experiment results show thatconvergence speed and stability are increased by improved genetic algorithm.