GEP Evolution Algorithm Based on Control of Mixed Diversity Degree
XUAN Shi-Bin1,2, LIU Yi-Guang1
1.College of Computer,Sichuan University,Chengdu 610064 2.College of Mathematics and Computer Science,Guangxi University for Nationalities,Nanning 530006
Abstract：Gene expression programming (GEP) is an evolution algorithm which has the problem of local optimization like other evolution algorithms. The general method to this problem is to keep the diversity degree of population in the evolution. A method is proposed for measuring the diversity of the population, and it merges characters of both population space and sample space. Based on the method for mergence measuring the diversity of population, GEP evolution algorithm with diversity control is proposed. The rival theory is introduced into the initialization of population. The experimental results show that the proposed algorithm efficiently avoids falling into early local optimization.
宣士斌，刘怡光. 基于混合差异度控制的基因表达式编程[J]. 模式识别与人工智能, 2012, 25(2): 186-194.
XUAN Shi-Bin, LIU Yi-Guang. GEP Evolution Algorithm Based on Control of Mixed Diversity Degree. , 2012, 25(2): 186-194.
[1] Diaz-Gomez P A,Hougen D F.Empirical Study: Initial Population Diversity and Genetic Algorithm Performance // Proc of the International Conference on Artificial Intelligence and Pattern Recognition.Orlando,USA,2007: 334-341 [2] Diaz-Gomez P A,Hougen D F.Initial Population for Genetic Algorithms: A Metric Approach // Proc of the International Conference on Genetic and Evolutionary Methods.Las Vegas,USA,2007: 43-49 [3] Hu Jianjun,Tang Changjie,Duan Lei,et al.The Strategy for Diversifying Initial Population of Gene Expression Programming.Chinese Journal of Computers,2007,30(2): 305-310 (in Chinese) (胡建军,唐常杰,段 磊,等.基因表达式编程初始种群的多样化策略.计算机学报,2007,30(2): 305-310) [4] Ventresca M,Tizhoosh H R.A Diversity Maintaining Population-Based Incremental Learning Algorithm.Information Sciences: An International Journal,2008: 178(21): 4038-4056 [5] Diaz-Gomez P A,Hougen D F.Initial Population Diversity Does Not Influence Performance // Proc of the International Conference on Genetic and Evolutionary Methods.Las Vegas,USA,2008: 55-63 [6] Nsakanda A L,Price W L,Diaby M,et al.Ensuring Population Diversity in Genetic Algorithms: A Technical Note with Application to the Cell Formation Problem.European Journal of Operational Research,2007,178(2): 634-638 [7] Li Fan,Xu Zhangyan.Measurement of Population Diversity in Genetic Algorithms.Journal of Huazhong University of Science and Technology,1999,27(7): 3-5 (in Chinese) (李 凡,徐章艳.遗传算法种群多样性的度量.华中理工大学学报,1999,27(7): 3-5) [8] Zhang Xiaohui,Dai Guanzhong,Xu Naiping.Study on Diversity of Population in Genetic Algorithms.Control Theory and Applications,1998,15(1): 17-20 (in Chinese) (张晓缋,戴冠中,徐乃平.遗传算法种群多样性的分析研究.控制理论与应用,1998,15(1): 17-20) [9] Liu Qihong,Tang Changjie,Hu Jianjun,et al.Gene Expression Programming Based on Diversity-Guided Grading Evolution.Journal of Sichuan University: Engineering Science Edition,2006,38(6): 108-113 (in Chinese) (刘齐宏,唐常杰,胡建军,等.多样性制导分段进化的基因表达式编程.四川大学学报:工程科学版,2006,38(6): 108-113) [10] Park T,Ryu K R.A Dual-Population Genetic Algorithm for Adaptive Diversity Control.IEEE Trans on Evolutionary Computation,2010,14(6): 865-884 [11] Zhang Chengwen,Su Sen,Chen Junliang.DiGA: Population Diversity Handling Genetic Algorithm for QoS-Aware Web Services Selection.Computer Communications,2007,30(5): 1082-1090 [12] Shimodaira,H.A Diversity-Control-Oriented Genetic Algorithm (DCGA): Performance in Function Optimization // Proc of the Congress on Evolutionary Computation.Seoul,South Korea,2001,1: 44-51 [13] Hu Jianjun,Tang Changjie,Peng Jing,et al.VPS-GEP: Skipping from Local Optimization Fast Algorithm.Journal of Sichuan University: Engineering Science Edition,2007,39(1): 128-133 (in Chinese) (胡建军,唐常杰,彭 京,等.快速跳出局部最优的VPS-GEP算法.四川大学学报:工程科学版,2007,39(1): 128-133) [14] Chang P C,Huang W H,Ting C J.Dynamic Diversity Control in Genetic Algorithm for Mining Unsearched Solution Space in TSP Problems.Expert Systems with Application,2010,37(3): 1863-1878 [15] Vinicius T D O,Renato T.Diversity Control in Genetic Algorithms for Protein Structure Prediction [EB/OL].[2010-10-23].https: // lrias.kuleuven.be/bitstream/123456789/274491/1/st03_02.pdf [16] Li Taiyong,Tang Changjie,Wu Jiang,et al.Adaptive Population Diversity Tuning Algorithm for Gene Expression Programming.Journal of University of Electronic Science and Technology of China,2010,39(2): 279-283 (in Chinese) (李太勇,唐常杰,吴 江,等.基因表达式编程种群多样性自适应调控算法.电子科技大学学报,2010,39(2): 279-283) [17] Hu Jianjun,Wu Xiaoyun.Superior Population Strategy in Gene Expression Programming.Journal of Chinese Computer System,2009,8(8): 1661-1662 (in Chinese) (胡建军,吴晓云.基因表达式编程中的优势种群产生策略.小型微型计算机系统,2009,8(8): 1661-1662) [18] Zhong Yixiao,Tang Changjie,Chen Yu,et al.Improve KDD Efficiency of Gene Expression Programming by Backtracking Strategy.Journal of Sichuan University: Natural Science Edition,2006,43(2): 299-304 (in Chinese) (钟义啸,唐常杰,陈 宇,等.提高基因表达式编程发现知识效率的回溯策略.四川大学学报:自然科学版,2006,43(2): 299-304) [19] Chen Hao,Li Ming,Zhang Ke.New Multi-Objectivization Evolutionary Algorithm Based on Individual Diversity.Control and Decision,2010,25(9): 1342-1348(in Chinese) (陈 昊,黎 明,张 可.一种基于多样性度量的多目标化进化算法.控制与决策,2010,25(9): 1342-1348) [20] Du Xin,Ding Lixin.About the Convergence Rates of a Class of Gene Expression Programming.Science China: Information Sciences,2010,40(1): 41-53 (in Chinese) (杜 欣,丁立新.一类基因表达式程序设计的收敛速度.中国科学:信息科学,2010,40(1): 41-53) [21] Yuan Changan,Tang Changjie,Zuo Jie,et al.Function Mining Based on Gene Expression Programming-Convergency Analysis and Remnant-Guided Evolution Algorithm.Journal of Sichuan University: Engineering Science Edition,2004,36(6): 100-105 (in Chinese)
2012, Vol. 25 
