【摘要】 遗传算法是建立在遗传学与自然选择基础上的自适应搜索过程．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，简单遗传算法存在着收敛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，改进遗传算法提高了收敛速度和稳定性．更多还原

【Abstract】 Genetic algorithm, based on the mechanism of genetics and natural selection, is an adaptive searching procedure. As a powerful approach applied to complex problems, genetic algorithm is a hot point for research on the fields of artificial intelligence and system optimization. However, simple genetic algorithm has drawbacks such as slow convergence and less stability in actual uses. To overcome these problems, crossover, mutation and reproduction operators as well as the procedure are improved based on introduction of the principles. Multimodal function optimization is performed to verify the feasibility and effectiveness. The experiment results show that convergence speed and stability are increased by improved genetic algorithm.更多还原