一种基因表达式程序设计的解码方法
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摘要
基因表达式程序设计(GEP)的解码通常仰赖表达式树的建立和后序遍历技术,因而解码复杂度、性能自然成为GEP应用的要害所在.在分析GEP基因型与表现型关系的基础上,提出一种称谓RL-GEP的新型解码方法.新方法基于0目操作符概念、工程应用与系统设计的原则,采用"一次读码多样本解析"和直接对线性编码的基因型实施解码等方法来提高解码效率,算法模型简单修改即可得到一种新型的传统GEP"无树解码"方法,具有良好的扩展性.RL-GEP不仅与传统GEP具有相同的表达能力与表现型空间,而且易于理解、应用和扩展.从求解回归问题的实验看来,本方法和经典GEP有相似问题求解的能力,但效率更高.
Decoding complexity and performance are of important concerns to applications of GEP( Gene Expression Programming),which generally decodes chromosomes based both on the construction of expression tree and post-order traversal. Taking into consideration the relationship between genotype and phenotype,a novel GEP decoding method,called RL-GEP,is proposed for interpreting genes in a straightforward manner in light of concept of 0-ary operator as well as principles of engineering applications and system designs. Genotype decoding methods such as 'An Analysis of Reading-Code M utl-Sample' and 'Direct Linear Coding' are adopted to improve the efficiency of decoding. A novel traditional GEP 'Non-Expression Tree' method can be produced through a simple modification of arithmetic model,which is of better expansibility and universality. RL-GEP not only possesses the same expressiveness and phenotypic space as that of GEP,but also is easy to understand and use,and convenient to be extended. Experimental results demonstrate that RL-GEP has similar capability to solve regression problems as traditional GEP,but is more efficient than the latter.
Decoding complexity and performance are of important concerns to applications of GEP( Gene Expression Programming),which generally decodes chromosomes based both on the construction of expression tree and post-order traversal. Taking into consideration the relationship between genotype and phenotype,a novel GEP decoding method,called RL-GEP,is proposed for interpreting genes in a straightforward manner in light of concept of 0-ary operator as well as principles of engineering applications and system designs. Genotype decoding methods such as 'An Analysis of Reading-Code M utl-Sample' and 'Direct Linear Coding' are adopted to improve the efficiency of decoding. A novel traditional GEP 'Non-Expression Tree' method can be produced through a simple modification of arithmetic model,which is of better expansibility and universality. RL-GEP not only possesses the same expressiveness and phenotypic space as that of GEP,but also is easy to understand and use,and convenient to be extended. Experimental results demonstrate that RL-GEP has similar capability to solve regression problems as traditional GEP,but is more efficient than the latter.
引文
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[5]Su Ke,Kong Sheng-li.Product material combination image Method based on GEP[C].The 19th International Conference on Industrial Engineering and Engineering M anagement,2013:235-243.
[6]Hosseini,Gandomi.Short-term load forecasting of power systems by gene expressionprogramming[J].Neural Computing&Applictions,2012,21(2):377-389.
[7]Nie Li,Gao Liang,Li Pei-gen,et al.A GEP-based reactive scheduling policies constructing approachfor dynamic flexible job shop scheduling problem with job releasedates[J].Journal of Intelligent M anufacturing,2013,24(4):763-774.
[8]Amir Hossein Gandomi,Amir Hossein Alavi,Ting T O,et al.Intelligent modeling and prediction of elastic modulusof concrete strength via gene expression programming[C].International Conference on Swarm Intelligence,2013:564-571.
[9]Aminuddin Ab Ghani,H Md.Azamathulla.Development of GEPbased functional relationship for sedimenttransport in tropical rivers[J].Neural Computing and Applications,2014,24(2):271-276.
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[11]Azamathulla H Md,Ahmad Z,Ab Ghani,Aminuddin.An expert system for predicting manning's roughness coefficient in open channels by using gene expression programming[J].Neural Comput&Applic,2013,23(5):1343-1349.
[12]Traore,Seydou,Guven,Aytac.Newalgebraic formulations of evapotranspiration extracted from gene-expression programming in the tropical seasonally dry regions of West Africa[J].Irrigation Science,2013,31(1):1-10.
[13]Deng Wei,He Pei,Qian Jun-yan.Multi-gene expression programming with depth-first decoding rinciple[J].Pattern Recognition and Artificial Intelligence,2013,26(9):819-828.
[14]Wang Chao,He Pei.Study on reutilization of GEP algorithm decoding structure[J].Application Research of Computers,2013,30(11):3244-3247.
[15]Ni Sheng-qiao,Tang Chang-jie,Yang Ning,et al.Gene expression programming algorithm based on multi-threading evahator[J].Journal of Computer Applications,2012,32(4):986-989.
[16]Jiang Da-zhi,Wu Zhi-jian,Kang Li-shan,et al.Newmethod used in gene expression programming:GRCM[J].Journal of System Simulation,2006,18(6):1466-1468.
[17]Chen Yu,Tang Chang-jie,Li Chuan,et al.LDecode:a novel decoding algorithm on gene expression programming with linear complexity[J].Journal of Sichuan University(Engineering Science Edition),2007,39(2):107-112.
[18]Li Chuan,Tang Chang-jie,Chen Yu,et al.Expressing genes without expression trees construction[J].Journal of Computer Applications,2008,28(5):1319-1321.
[19]Chen An-sheng,Cai Zhi-hua,Gu Qiong,et al.Novel GEP algorithm and its application[J].Application Research of Computers,2007,24(6):98-100.
[20]Wang Xiao,He Pei.Newdecoding method of GEP[J].Computer Engineering and Applications,2012,48(3):43-45.
[21]Xie Da-tong,Chen Qiao-yun.Two decoding methods of gene expression programming[J].Computer Engineering,2008,34(23):210-213.
[13]邓薇,何锫,钱俊彦.深度优先的多基因表达式程序设计[J].模式识别与人工智能,2013,26(9):819-828.
[14]王超,何锫.GEP算法解码结构复用研究[J].计算机应用研究.2013,30(11):3244-3247.
[15]倪胜巧,唐常杰,杨宁,等.基于多线程评估的基因表达式编程算法[J].计算机应用,2012,32(4):986-989.
[16]姜大志,吴志健,康立山,等.基因表达式程序设计的GRCM方法[J].系统仿真学报,2006,18(6):1466-1468.
[17]陈瑜,唐常杰,李川,等.LDecode:具有线性复杂度的GEP适应度评价算法[J].四川大学学报:工程科学版,2007,39(2):107-112.
[18]李川,唐常杰,陈瑜,等.无表达式树的基因表达[J].计算机应用,2008,28(5):1319-1321.
[19]陈安升,蔡之华,谷琼,等.一种新型的GEP算法及应用研究[J].计算机应用研究,2007,24(6):98-100.
[20]王晓,何锫.一种新型GEP解码方法[J].计算机工程与应用,2012,48(3):43-45.
[21]谢大同,陈巧云.基因表达式编程的2种解码方法[J].计算机工程,2008,34(23):210-213.
[2]Ferreira C.Gene expression programming[M].Portugal:Angorado Heroismo,2002.
[3]Azamathulla,Robert.Use of gene-expression programming to estimate manning's roughness coefficient for high gradient streams[J].Water Resources M anagement,2013,27(3):715-729.
[4]¨Ozlem Terzi.Daily pan evaporation estimation using gene expressionprogramming and adaptive neural-based fuzzy inference system[J].Neural Computing&Applications,2013,23(3):1035-1044.
[5]Su Ke,Kong Sheng-li.Product material combination image Method based on GEP[C].The 19th International Conference on Industrial Engineering and Engineering M anagement,2013:235-243.
[6]Hosseini,Gandomi.Short-term load forecasting of power systems by gene expressionprogramming[J].Neural Computing&Applictions,2012,21(2):377-389.
[7]Nie Li,Gao Liang,Li Pei-gen,et al.A GEP-based reactive scheduling policies constructing approachfor dynamic flexible job shop scheduling problem with job releasedates[J].Journal of Intelligent M anufacturing,2013,24(4):763-774.
[8]Amir Hossein Gandomi,Amir Hossein Alavi,Ting T O,et al.Intelligent modeling and prediction of elastic modulusof concrete strength via gene expression programming[C].International Conference on Swarm Intelligence,2013:564-571.
[9]Aminuddin Ab Ghani,H Md.Azamathulla.Development of GEPbased functional relationship for sedimenttransport in tropical rivers[J].Neural Computing and Applications,2014,24(2):271-276.
[10]Georgios Sermpinis,Anastasia Fountouli,Konstantinos Theofilatos,et al.Gene expression programming and trading strategies[C].Artificial Intelligence Applications and Innovations(AIAI),2013:497-505.
[11]Azamathulla H Md,Ahmad Z,Ab Ghani,Aminuddin.An expert system for predicting manning's roughness coefficient in open channels by using gene expression programming[J].Neural Comput&Applic,2013,23(5):1343-1349.
[12]Traore,Seydou,Guven,Aytac.Newalgebraic formulations of evapotranspiration extracted from gene-expression programming in the tropical seasonally dry regions of West Africa[J].Irrigation Science,2013,31(1):1-10.
[13]Deng Wei,He Pei,Qian Jun-yan.Multi-gene expression programming with depth-first decoding rinciple[J].Pattern Recognition and Artificial Intelligence,2013,26(9):819-828.
[14]Wang Chao,He Pei.Study on reutilization of GEP algorithm decoding structure[J].Application Research of Computers,2013,30(11):3244-3247.
[15]Ni Sheng-qiao,Tang Chang-jie,Yang Ning,et al.Gene expression programming algorithm based on multi-threading evahator[J].Journal of Computer Applications,2012,32(4):986-989.
[16]Jiang Da-zhi,Wu Zhi-jian,Kang Li-shan,et al.Newmethod used in gene expression programming:GRCM[J].Journal of System Simulation,2006,18(6):1466-1468.
[17]Chen Yu,Tang Chang-jie,Li Chuan,et al.LDecode:a novel decoding algorithm on gene expression programming with linear complexity[J].Journal of Sichuan University(Engineering Science Edition),2007,39(2):107-112.
[18]Li Chuan,Tang Chang-jie,Chen Yu,et al.Expressing genes without expression trees construction[J].Journal of Computer Applications,2008,28(5):1319-1321.
[19]Chen An-sheng,Cai Zhi-hua,Gu Qiong,et al.Novel GEP algorithm and its application[J].Application Research of Computers,2007,24(6):98-100.
[20]Wang Xiao,He Pei.Newdecoding method of GEP[J].Computer Engineering and Applications,2012,48(3):43-45.
[21]Xie Da-tong,Chen Qiao-yun.Two decoding methods of gene expression programming[J].Computer Engineering,2008,34(23):210-213.
[13]邓薇,何锫,钱俊彦.深度优先的多基因表达式程序设计[J].模式识别与人工智能,2013,26(9):819-828.
[14]王超,何锫.GEP算法解码结构复用研究[J].计算机应用研究.2013,30(11):3244-3247.
[15]倪胜巧,唐常杰,杨宁,等.基于多线程评估的基因表达式编程算法[J].计算机应用,2012,32(4):986-989.
[16]姜大志,吴志健,康立山,等.基因表达式程序设计的GRCM方法[J].系统仿真学报,2006,18(6):1466-1468.
[17]陈瑜,唐常杰,李川,等.LDecode:具有线性复杂度的GEP适应度评价算法[J].四川大学学报:工程科学版,2007,39(2):107-112.
[18]李川,唐常杰,陈瑜,等.无表达式树的基因表达[J].计算机应用,2008,28(5):1319-1321.
[19]陈安升,蔡之华,谷琼,等.一种新型的GEP算法及应用研究[J].计算机应用研究,2007,24(6):98-100.
[20]王晓,何锫.一种新型GEP解码方法[J].计算机工程与应用,2012,48(3):43-45.
[21]谢大同,陈巧云.基因表达式编程的2种解码方法[J].计算机工程,2008,34(23):210-213.
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