BI-DISTINCTIVE-POPULATION CO-EVOLUTIONARY GENETIC ALGORITHM FOR TRAVELING SALESMAN PROBLEM

摘要：

This paper introduces a new co-evolutionary strategy for genetic algorithm based on bi-distinctive populations. One of the two populations adopts permutation encoding; the other one adopts edge encoding. Each of two populations evolutes separately, and exchange critical information after evolution. Population with permutation encoding could avoid premature convergence by stochastically selecting reference optimization edge set from original edge set or edge sets established by individuals from population with edge encoding. The analyses and experimental results show that new genetic algorithm could converge to global optimal solution of arbitrary traveling salesman problems, whose scales are less than 1,500, from TSPLIB95 with shorter time than congeneric algorithms.

关键词： Genetic algorithm traveling salesman problem bi-distinctive population co-evolution strategy information ezchange

作者: DONG-MEI LIN DONG WANG

作者单位: Center of Information and Education Technology, Foshan University, Foshan, Guangdong 528000, China Department of Computer Science and Technology, Foshan University, Foshan, Guangdong 528000, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 924-928

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）