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基因表达式编程ORF过滤算子的设计和实现 被引量：4

Design and Implementation of ORF Filter in Gene Expression Programming
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摘要 基因表达式编程(GEP)的个体代表了问题的候选解。在缺乏先验知识的情况下,个体长度的设定是个"两难"问题,过长或过短都会降低GEP的效率。对此,分析了个体长度对GEP求解效率的影响;设计了开放阅读框(ORF)过滤算子根据最优个体的进化历程动态调节个体的有效编码区域;验证了ORF过滤算子的有效性,实验结果表明,在同样的进化代数内,引入ORF过滤算子,GEP能进化出更高适应度的最优解且减少平均运行时间17.0%。 The individuals of Gene Expression Programming （GEP） represent the candidate solutions of the problem. The determining of the length of an individual is a dilemma when there is no prior knowledge available. Either long or short length decreases the efficiency of GEP. The efficiency impact of the individual＇ s length was analyzed, Open Reading Frame （ORF） filter was designed to modify the coding region dynamically based on the evolution progress of the best individual, and the effectiveness of ORF filter was demonstrated. The experiments showed that solutions with higher fitness can be found and the average runtime is reduced by 17.0% in the same number of generations by introducing ORF filter into original GEP.
作者 段磊 唐常杰 刘胤田 左劼 吴江
机构地区 四川大学计算机学院
出处 《四川大学学报（工程科学版）》 EI CAS CSCD 北大核心 2007年第6期102-106,共5页 Journal of Sichuan University (Engineering Science Edition)
基金 国家自然科学基金资助项目(60473071) 高等学校博士学科点专项科研基金SRFDP(20020610007) 四川省青年软件创新工程项目(2005AA0807)
关键词 基因表达式编程 开放阅读框 个体 Gene Expression Programming Open Reading Frame individual
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论]
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参考文献11

  • 1Ferreira C, Gene Expression Programming: a new adaptive algorithm for solvlng problems [J]. Complex Systems, 2001,13(2): 87-129.
  • 2Ferreira C: Gene Expression Programming: mathematical modeling by an artificial intelligence [M]. Angra do Heroismo, Portugal, 2002.
  • 3Ferreira C. Discovery of the Boolean functions to the best density-classification rules using Gene Expression Programming [C]//EuroGP 2002, LNCS 2278,2002,51-60.
  • 4Zuo Jie, Tang Changjie, Zhang Tianqing. Mining predicate association rule by Gene Expression Programming [C]// WAIM 2002. LNCS 2419,2002,92-103.
  • 5段磊,唐常杰,左劼,陈宇,钟义啸,元昌安.基于基因表达式编程的抗噪声数据的函数挖掘方法[J].计算机研究与发展,2004,41(10):1684-1689. 被引量：40
  • 6Lopes H S, Weinert W R. EGIPSYS: an enhanced Gene Expression Programming approach for symbolic regression problems [J]. International Journal of Applied Mathematics and Computer Science,2004,14(3):375-384.
  • 7Zuo Jie, Tang Changjie, Li Chuan, et al. Time series prediction based on Gene Expression Programming [C]// WAIM 2004. LNCS 3129, 2004:55-64.
  • 8Zhou Chi, Xiao Weimin, Tirpak T M,et al. Evolution accurate and compact classification rules with Gene Expression Programming [J]. IEEE Trans on Evolutionary Computation,2003,7(6):519-531.
  • 9Duan Lei, Tang Changjie, Zhang Tianqing, et al. Distance guided classification with Gene Expression Programming [C]//ADMA 2006, LNAI 4093, 2006,239-246.
  • 10Weinert W R, Lopes H S. GEPCLASS: a classification rule discovery tool using Gene Expression Programming [C]//ADMA 2006, LNAI 4093, 2006:871-880.

二级参考文献8

  • 1Candida Ferreira. Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems, 2001, 13(2):87～ 129
  • 2C Ferreira. Gene Expression Programming in Problem Solving [OL]. http://www. gene-expression-programming. com/gep/webpapers/Ferreira-WSC2001/Introduction. htm, 2001
  • 3C Ferreira. Mutation, Transposition, and recombination: An analysis of the evolutionary dynamics. The 6th Joint Conf on Information Sciences, the 4th Int'l Workshop on Frontiers in Evolutionary Algorithms, Research Triangle Park, North Carolina, USA, 2002
  • 4C Ferreira. Discovery of the boolean functions to the best densityclassification rules using gene expression programming. In: Proc of the 4th European Conf on Genetic Programming(EuroGP 2002),LNCS 2278. Berlin: Springer-Verlag, 2002. 51～60
  • 5Zuo Jie, Tang Changjie, Zhang Tianqing. Mining predicate association rule by gene expression programming. In: Proc of the 3rd Int' 1 Conf for Web Information Age 2002 (WAIM02), LNCS 2419. Berlin: Springer-Verlag, 2002. 92～103
  • 6Zuo Jie, Tang Changjie, Li Chuan, et al. Time series prediction based on gene expression programming. In: Proc of the 5th Int'l Conf for Web Information Age 2004 (WAIM04), LNCS 3129.Berlin: Springer-Verlag, 2004. 55～64
  • 7Jiawei Han, Micheline Kambr. Data Mining-Concepts and Techniques. Beijing: Higher Education Press, 2001. 110 ～ 112
  • 8黄晓冬 唐常杰.基于基因表达式编程的函数关系发现方法[J].计算机科学,2003,30:278-282.

共引文献39

同被引文献74

引证文献4

二级引证文献21

四川大学学报（工程科学版）
2007年 第6期

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