个人简介

马晓亮，男，博士，深圳大学助理教授。 于2014年12月在西安电子科技大学获工学博士学位 于2015年7月至今，深圳大学工作。 多年来一直从事人工智能、进化算法、多目标优化、大规模优化、路径优化等方向。发表论文20余篇，1篇论文入选ESI高被引论文，Google学术引用达700余次。 2017年获得深圳市后备人才计划。

研究领域

从事人工智能、进化算法、多目标优化、大规模优化、路径优化等方向

主持的基金项目 ： [1] 国家自然科学基金 青年基金 (No. 61603259) 面向配送路径优化问题的传输学习和多目标自适应模因计算方法研究. 2017.1-2019.12, 项目经费19万元 [2] 中国博士后科学基金 (No. 2016M592536) 基于多目标自适应Memetic算法的大规模车辆调度研究. 2016.1-2017.12, 项目经费5万元

近期论文

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代表论文作: [1] Xiaoliang Ma, Xiaodong Li, Qingfu Zhang, Ke Tang, Zhengping Liang, Weixin Xie, Zexuan Zhu*. A Survey on Cooperative Co-evolutionary Algorithms. IEEE Transactions on Evolutionary Computation. 2018, accepted, 10.1109/TEVC.2018.2868770. (中科院1区，IF: 8.124) [2] Xiaoliang Ma, Fang Liu, Yutao Qi, Xiaodong Wang, Lingling Li, Licheng Jiao, Minglei Yin, Maoguo Gong. A multiobjective evolutionary algorithm based on decision variable analyses for multiobjective optimization problems with large-scale variables. IEEE Transactions on Evolutionary Computation, 2016, 20(2): 275-298. (中科院1区, IF: 8.124) [3] Xiaoliang Ma, Qingfu Zhang, Guangdong Tian, Junshan Yang, and Zexuan Zhu. On Tchebycheff decomposition approaches for multiobjective evolutionary optimization. IEEE Transactions on Evolutionary Computation, 2018, 22(2): 226-244. (中科院1区, IF: 8.124) [4] Xiaoliang Ma, Fang Liu, Yutao Qi, Lingling Li, Licheng Jiao, Meiyun Liu, Jianshe Wu. MOEA/D with Baldwinian learning inspired by the regularity property of continuous multiobjective problem. Neurocomputing. 2014, 145: 336-352. (中科院2区, IF: 3.317) [5] Xiaoliang Ma, Fang Liu, Yutao Qi. etal. MOEA/D with opposition-based learning for multiobjective optimization problem. Neurocomputing. 2014, (146):48-64. (中科院2区, IF: 3.317) [6] Xiaoliang Ma, Yutao Qi, Lingling Li, Maoguo Gong, Minglei Yin, Lingling Li, Licheng Jiao, Jianshe Wu. MOEA/D with uniform decomposition measurements for many-objective problems. Soft Computing. 2014.12, 18(12): 2541-2564. (中科院3区, IF: 2.472) [7] Xiaoliang Ma, Fang Liu, Yutao Qi, Lingling Li, Licheng Jiao, Xiaozheng Deng, Xiaodong Wang, Bei Dong, Zhanting Hou, Yongxiao Zhang, Jianshe Wu. MOEA/D with biased weight adjustment inspired by user-preference and its application on multi-objective reservoir flood control problem. Soft Computing, 20(12): 4999-5023, 2016.(中科院3区, IF: 2.472) [8] Yutao Qi, Xiaoliang Ma, Fang Liu, Licheng Jiao, Jianyong Sun, Jianshe Wu. MOEA/D with adaptive weight adjustment. Evolutionary Computation. 2014, 22(2): 231-264. (中科院2区, IF: 3.826) [9] Yutao Qi, Liang Bao, Xiaoliang Ma, Qiguang Miao, Xiaodong Li. Self-adaptive multi-objective evolutionary algorithm based on decomposition for large-scale problems: A case study on reservoir flood control operation. Information Sciences. 367-368(1): 529-549, 2016. (中科院1区, IF: 4.832) [10] Yutao Qi, Qingfu Zhang, Xiaoliang Ma, Yining Quan, Qiguang Miao. Utopian point based decomposition for multi-objective optimization problems with complicated Pareto fronts. Applied Soft Computing, 844-859, 2017. (中科院2区) [11] Yang Junshan, Zhou Jiarui, Zexuan Zhu, Xiaoliang Ma, Zhen Ji. Iterative Ensemble Feature Selection for Multiclass Classification of Imbalanced Microarray Data. Journal of Biological Research, 2016, 23(13):1-9.