摘要
针对遗传算法应用的局限性,引入新的种群择优交叉运算、变异运算、遗传边界算子和相互学习过程的思想,提出一种新型混合遗传算法,提高了算法的收敛速度和稳定性,数值算例验证了该算法的有效性和实用性。
To overcome the limitation of the basic genetic algorithm, the paper presents a new hybrid genetic algorithm that combines self-adapting crossover, stochastic mutation operators, boundary operators and learning process. Comparing with the basic genetic algorithm, the improved algorithm has better velocity of convergence and higher stability, the numerical examples illustrate the validity and effciency of the new hybrid genetic algorithm.
出处
《计算机与现代化》
2012年第8期28-31,36,共5页
Computer and Modernization
基金
教育部人文社科基金资助项目(12YJCZH303)
中国博士后科学基金资助项目(2011M501149)
关键词
遗传算法
交叉
变异
相互学习
genetic algorithm
crossover
mutation
learning process
