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An effective genetic algorithm with uniform crossover for Bi-objective unconstrained binary quadratic programming problem
Huo, Chao1; Zeng, Rong-Qiang1,2; Wang, Yang3; Shang, Ming-Sheng4
2016
摘要The unconstrained binary quadratic programming problem is one of the most studied NP-hard problem with its various practical applications. In this paper, we propose an effective multi-objective genetic algorithm with uniform crossover for solving bi-objective unconstrained binary quadratic programming problem. In this algorithm, we integrate the uniform crossover within the hypervolume-based multiobjective optimization framework for further improvements. The computational studies on 10 benchmark instances reveal that the proposed algorithm is very effective in comparison with the original multi-objective optimization algorithms. © Springer International Publishing AG 2016.
语种英语
DOI10.1007/978-3-319-46257-8_7
会议(录)名称17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016
页码58-67
通讯作者Zeng, Rong-Qiang (zrq@home.swjtu.edu.cn)
收录类别EI
会议地点Yangzhou, China
会议日期October 12, 2016 - October 14, 2016