计算机工程 ›› 2017, Vol. 43 ›› Issue (12): 147-154.doi: 10.3969/j.issn.1000-3428.2017.12.028

• 人工智能及识别技术 • 上一篇    下一篇

基于概率分布的动态矩阵演化算法

谭阳 1,2,方颂 2,陈琳 2   

  1. (1.湖南师范大学 数学与计算机科学学院,长沙 410081; 2.湖南广播电视大学 网络技术系,长沙 410004)
  • 收稿日期:2016-10-24 出版日期:2017-12-15 发布日期:2017-12-15
  • 作者简介:谭阳(1979—),男,副教授,主研方向为智能计算;方颂,高级工程师;陈琳,讲师。
  • 基金项目:
    国家自然科学基金(10971060);湖南省教育厅科研项目(14C0781,15C0928)。

Dynamic Matrix Evolutionary Algorithm Based on Probability Distribution

TAN Yang  1,2,FANG Song  2,CHEN Lin  2   

  1. (1.College of Mathematics and Computer Science,Hunan Normal University,Changsha 410081,China; 2.Department of Network Technology,Hunan Radio and TV University,Changsha 410004,China)
  • Received:2016-10-24 Online:2017-12-15 Published:2017-12-15

摘要:

传统演化算法通常以宏观层面的种群之间或个体之间的相互作用来进行协同演化,较少考虑个体基因编码在微观层面进行局部优化时的相互作用。针对该情况,提出基于种群基因分布结构的动态矩阵演化算法。利用二进制基因矩阵的方式构建种群个体,结合基因编码差异及适应度评价种群个体,通过对比种群基因列决定个体基因结构调整的位置,并根据优势种群的基因结构产生下一代个体,通过微观层面上基因位之间的协同作用引导种群的演化。实验结果表明,该算法对于中、高维函数均表现出良好的优化性能,同时能较好地平衡宏观全局优化和微观局部优化之间的关系。

关键词: 演化算法, 基因编码, 矩阵, 概率分布, 全局优化

Abstract: Traditional evolutionary algorithms usually use the macro-level interaction between individuals or populations to co-evolution,with less consideration in the individual gene encoding in micro level when interacting local optimization.Aiming at this situation,this paper proposes Dynamic Matrix Evolutionary Algorithm(DMEA) based on population gene distribution structure.It uses binary gene matrix to construct individual genes.It evaluates population individuals according to the difference of genes encoding and fitness and decide the adjustment position of individual gene structure by comparing the population gene series.According to structure of the dominant population,it generates the next generation of individuals and guides the evolution of the population by the synergistic effect of micro-level gene location.Experimental results show that the proposed algorithm has good performance in the optimization of mid-dimensional or high-dimensional functions,and it also can better balance the relationship between the microscopic optimization and the macroscopic optimization.

Key words: evolutionary algorithm, gene encoding, matrix, probability distribution, global optimization

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