数值优化中三父体杂交的自适应遗传算法(英文)  

Adaptive genetic algorithm with three-parent crossover for numerical optimization

作　　者：张青莲 张新[1] 

机构地区：[1]天津师范大学天津市无线移动通信与无线电能传输重点实验室,天津300387

出　　处：《天津师范大学学报（自然科学版）》2017年第5期55-59,共5页Journal of Tianjin Normal University：Natural Science Edition

基　　金：Supported by the National Natural Science Foundation of China(61603275);the Applied Basic Research Program of Tianjin(15JCYBJC51500)

摘　　要：遗传算法(GA)是一种适合于数值优化的算法原型,基于1个三父体交叉(TPC)和1个多样性算子虽然可使GA的性能得到很大改进,但仍受制于几个算法参数.在此基础上,对TPC和多样性算子中算法参数的自适应遗传算法进行研究.算法的关键参数在每次迭代中由正态分布生成,并在1组13个数学函数集上施行.对原算法与添加参数适应算法的结果在函数f_1~f_(13)上进行对比,并给出了f_4和f_(10)的收敛过程,分析表明自适应GA-TPC算法比原算法在解决具体问题时更加高效和稳定.Genetic algorithm（ GA） is a suitable algorithm prototype for numerical optimization. Based on a three-parentcrossover （ TPC）and a diversity operator, the performance of GA is greatly improved, though it is limited by a few algorithmicparameters. The adaptation of algorithmic parameters in TPC and diversity operator is investigated. The key parameters aregenerated based on normal distribution in each iteration. Experiments are conducted on a set of 13 mathematical functions.The results of the algorithm with and without parameter adaptation are compared from 1 to 13. Furthermore, the convergenceprocesses of 4 and 10 are presented which show that the adaptive GA-TPC is more efficient and more robust than the GA-TPCalgorithm.

关 键 词：遗传算法 参数控制 交叉 数值优化 

分 类 号：O242.23[理学—计算数学]