面向复杂优化问题的多目标进化算法
Multi-objective evolutionary algorithms for solving complex optimization problems
张兴义   
报告人照片   张兴义,博士,教授,博士生导师,安徽大学计算机科学与技术学院生物智能与知识发现研究所(BIMK)所长。2009年博士毕业于华中科技大学。从2009年7月开始,在安徽大学计算机科学与技术学院从事教学与科研工作。2013-2014年在英国University of Surrey访问一年。主要研究领域为非传统计算模型与算法、多目标优化算法及应用、复杂网络分析等。作为项目负责人,先后主持国家自然科学基金面上项目2项,青年项目1项,安徽省教育厅重点项目1项。在IEEE TEVC、IEEE TNNLS、IEEE TC和IEEE Computational Intelligence Magazine等高水平学术刊物上发表论文40多篇,其中发表SCI收录30多篇。为国际期刊Complex & Intelligent Systems和International Journal of Bio-Inspired Computation编委。
  Multi-objective evolutionary algorithms have been verified to be a useful technology for solving optimization problems during the last two decades, however, much work still deserves further investigations when addressing complex optimization tasks. In this talk, I will first briefly introduce the multi-objective evolutionary algorithms, and then mainly focus on three multi-objective evolutionary algorithms recently suggested by us to tackle complex optimization problems. The three works included in this presentation are: 1) a knee point driven evolutionary algorithm for many-objective optimization problems, 2) a decision variable clustering based evolutionary algorithm for large-scale optimization problems, and 3) a multi-objective evolutionary algorithm for task-oriented pattern mining task.
报告时间:2017年11月08日09时00分    报告地点:科大西区电二楼208会议室
报名截止日期:2017年11月08日    可选人数:40