摘要
本文提出一种遗传进化规划,该方法结合了遗传算法和进化规划两种算法的优点,在进化过程中遗传算法的交换率、变异率和进化规划的变异规则均根据种群的进化信息而自适应变化。该方法不仅能够加快算法的收敛速度,而且能够有效地保持种群的多样性。用该方法求解混合非线性整数规划问题,计算机仿真实验结果表明是非常有效的.
A genetic evolutionary programming is proposed in the paper, which combines the advantages of genetic algorithm and evolutionary programming. In the evolving process, the exchange rate and mutate rate of genetic algorithm and the mutate rules of evolutionary programming are changed self-adaptively according to evolution information of the population. It can not only keep the population diversity but also has quicker convergence speed. It is applied to integer programming. Computer simulation results show its validity.
出处
《计算机科学》
CSCD
北大核心
2005年第12期24-26,33,共4页
Computer Science
关键词
遗传算法
进化规划
进化性
混合非线性整数规划问题
熵
Genetic algorithm, Evolutionary programming, Capability of evolution, Mixed integer non-linear programruing problems, Entropy
