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Multi-parent Crossover Based Genetic Algorithm for Bi-Objective Unconstrained Binary Quadratic Programming Problem
Huo, Chao1; Zeng, Rongqiang1,2; Wang, Yang3; Shang, Mingsheng4
2016
摘要In this paper, we present a multi-parent crossover based genetic algorithm for the bi-objective unconstrained binary quadratic programming problem, by integrating the multi-parent crossover within the framework of hypervolume-based multi-objective optimization algorithm. The proposed algorithm employs a multi-parent crossover operator to generate the offspring solutions, which are used to further improve the quality of Pareto approximation set. Experimental results on 10 benchmark instances demonstrate the efficacy of our proposed algorithm compared with the original multi-objective optimization algorithms. © Springer Nature Singapore Pte Ltd. 2016.
语种英语
DOI10.1007/978-981-10-3614-9_2
会议(录)名称11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016
页码10-19
通讯作者Zeng, Rongqiang (zrq@home.swjtu.edu.cn)
收录类别EI
会议地点Xian, China
会议日期October 28, 2016 - October 30, 2016