A New Algorithm of Automatic Programming: GEGEP

  • Xin Du
  • Yueqiao Li
  • Datong Xie
  • Lishan Kang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)


Gene Expression Programming (GEP) has wide searching ability, simple representation, powerful genetic operators and the creation of high levels of complexity. However, it has some shortcomings, such as blind searching and when dealing with complex problems, its genotype under Karva notation does not allow hierarchical composition of the solution, which impairs the efficiency of the algorithm. So a new automatic programming method is proposed: Gene Estimated Gene Expression Programming(GEGEP) which combines the advantages of Estimation of Distribution Algorithm (EDA) and basic GEP. Compared with basic GEP, it mainly has the following characteristics: First, improve the gene expression structure, the head of gene is divided into a head and a body, which can be used to introduce learning mechanism. Second, the homeotic gene which is also composed of a head, a body and a tail is used which can increase its searching ability. Third, the idea of EDA is introduced, which can enhance its learning ability and accelerate convergence rate. The results of experiments show that GEGEP has better fitting and predicted precision, faster convergence speed than basic GEP and traditional GP.


Gene Expression Programming Genetic Programming Gene Estimated Gene Expression Programming Estimation of Distribution Algorithm 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ferreira, C.: Gene Expression Programming: a New Adaptive Algorithm for Solving Problems. Complex Systems 13(2), 87–129 (2001)zbMATHMathSciNetGoogle Scholar
  2. 2.
    Ferreira, C.: Gene expression programming [M]. Portugal, Angra do Heroismo (2002)Google Scholar
  3. 3.
    Ferreira, C.: Gene expression programming in problem solving [A]. In: 6th Online World Conference on Soft Computing in Industrial Applications [C] (2001)Google Scholar
  4. 4.
    Li, X., Zhou, C., Xiao, W., Nelson, P.C.: Prefix Gene Expression Programming. In: Genetic and Evolutionary Computation Conference (GECCO 2005), June 25-29, 2005, Washington (2005)Google Scholar
  5. 5.
    Larrañaga, P., Lozano, J.A.: Estimation of distribution alg- orithms. A new tool for evolutionary computation. Kluwer Academic Publishers, Dordrecht (2001)Google Scholar
  6. 6.
    Zhao, C.Y., Yuan, X.G., Sun, J.B.: Application of Genetic Progr- amming to Predicting the Amount of Gas Emitted from Coal Face [J]. Journal of Basic Science and Engineering 7(4), 387–392 (1999)Google Scholar
  7. 7.
    Li, Q., Cai, Z.H., Zhu, L., Zhao, S.Y.: Application of Gene Expr- ession Programming in Predicting the Amount of Gas Emitted from Coal Face [J]. Journal of Basic Science and Engineering 3(12), 49–54 (2004)Google Scholar
  8. 8.
    Zhihua, C., Siwei, J., Li, Z., Yuanyuan, G.: A Novel Algorithm of Gene Expression Programming Based on Simulated Annealing. In: International Symposium on Intelligence Computation & Applications [C], pp. 605–610 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xin Du
    • 1
    • 2
  • Yueqiao Li
    • 1
  • Datong Xie
    • 1
  • Lishan Kang
    • 1
    • 3
  1. 1.Department of Computer Science and TechnologyChina University of GeosciencesWuhanChina
  2. 2.Department of Information and engineeringShijiazhuang University of EconomicsShijiazhuangChina
  3. 3.State Key Laboratory of Software EngineeringWuhan UniversityWuhanChina

Personalised recommendations