Automatic Complex Function Discovery based on Multi Expression Gene Programming

Dai Shu-cheng; TANG Chang-jie; 朱明放

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Automatic Complex Function Discovery based on Multi Expression Gene Programming

更新时间：2023-11-28

- Automatic Complex Function Discovery based on Multi Expression Gene Programming
- Advanced Engineering Sciences Vol. 40, Issue 6, Pages: 121-126(2008)
- 作者机构：
  
  1. 四川大学
  2. 四川大学计算机学院四川,成都,610065
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2008，
  
  Published Online：24 June 2008，
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代术成,唐常杰,朱明放.基于多表达式基因编程的复杂函数挖掘算法[J].工程科学与技术,2008,40(6):121-126.

Dai Shu-cheng, TANG Chang-jie, 朱明放. Automatic Complex Function Discovery based on Multi Expression Gene Programming. [J]. Advanced Engineering Sciences 40(6):121-126(2008)
代术成,唐常杰,朱明放.基于多表达式基因编程的复杂函数挖掘算法[J].工程科学与技术,2008,40(6):121-126. DOI：

Dai Shu-cheng, TANG Chang-jie, 朱明放. Automatic Complex Function Discovery based on Multi Expression Gene Programming. [J]. Advanced Engineering Sciences 40(6):121-126(2008) DOI：

摘要

中文摘要: 传统的基因表达式编程(Gene Expression Programming) 挖掘复杂函数时，存在进化辈数过大、无法跳出局部最优解等问题，本文提出了基于多表达式基因编程的遗传进化算法，提高GEP的全局寻优能力

主要工作包括：(1)提出了一种新的多表达式基因编程的遗传进化算法(Multi Expression Gene Programming

MEGP); (2)建立了同一染色体内基因多层次编码、解码模型; (3)理论上分析并比较了MEGP算法的表达空间复杂性; (4)实现了多表达染色体遗传进化算法和染色体适应度评价算法. 实验表明

在解决一般函数挖掘问题中

MEGP成功率是传统GEP的2-4倍.

Abstract

Abstract:For complex function mining

traditional gene expression programming (GEP) needs large number of evolutionary generations and may plunge into local optimum. To solve this problem

this paper presents a novel evolutionary algorithm based on multiple expression genes programming (MEGP). The main contributions include: (a) provide a novel gene hierarchical representation model to encode solutions of complex function finding; (b) propose a chromosome architecture that allows of a genome with multiple candidate expressions; (c) theoretically analyze and compare the expression space of MEGP algorithm with traditional GEP; (d) implement the MEGP algorithm and the chromosome fitness evaluation algorithm. Extensive experiments show that the success rate of MEPG is 2-4 times of traditional GEP.

关键词

基因表达式编程多表达式函数发现遗传进化

Keywords

gene expression programmingMulti ExpressionFunction FindingGenetic Algorithm

references

Ferreira C,Gene Expression Programming:Mathematical modeling by an artificial intelligence,Berlin:springer-verlag,2006.

Bautu E,Bautu A,Luchian H,Symbolic regression on noisy data with genetic and Gene Expression Programming,2005.

Gan Z H,Yang Z K,Li G B,Automatic modeling of complex functions with clonal selection-based Gene Expression Programming,2007.

Zhou C,Xiao W M,Nelson P C,Evolving classification rules with Gene Expression Programming,IEEE Transactions on Evolutionary Computation,2003(6).

Zeng T,Tang C J,Liu Y T,Mining h-dimensional enhanced semantic association rule based on immune-based Gene Expression Programming,Germany:Springer,2006.

Lopes H S,Weinert W R,A gene expression programming system for time series modeling,2004.

Oltean M,Dumitrescu D,Multi Expression Programming,Romania:Babes-Bolyai Univ,2006.

Goldberg D E,Genetic algorithms in search,optimization and machine learning,Addison-Weskey,1989.

Koza J R,Genetic programming:On the programming of computers by means of natural selection,Cambridge,ma:the Mit Press,1992.

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College of Water Resource and Hydropower, Sichuan Univ.

四川大学计算机学院四川成都６１００６5

四川大学计算机学院四川成都 6100642.中国工程物理研究院化工材料研究所四川

西南科技大学计算机科学与技术学院

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