Gene Expression Programming with DAG Chromosome

  • Hui-yun Quan
  • Guangyi Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4683)

Abstract

GEP(Gene Expression Programming) is applied to comprehensive fields such as Symbolic Regression,Parameter Optimization, Cellular Automate etc[2].With Kara-style chromosome,GEP can only express tree phynotype. This limits the expressiveness of the program that can be evolved. In this paper, a DAG(Directed Acyclic Graph) chromosome is integrated into GEP without increasing the computational complexity of fitness evaluation while improving the expressiveness of gene expression programming.

Keywords

gene expression programming directed acyclic graph symbolic regression 

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References

  1. 1.
    Mihai, O.: Solving Even-Parity Problems using Multi Expression Programming. In: The 7th Joint Conference on Information Science, September 26-30, pp. 315–318. Research Triangle Park, North Carolina (2003)Google Scholar
  2. 2.
    Ferreira, C.: Gene expression programming:Mathematical Modeling by an Artificial Intelligence. Angra do Heroismo, Portugal (2002)Google Scholar
  3. 3.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)MATHGoogle Scholar
  4. 4.
    Kahrs, S.: Genetic programming with primitive recursion. In: GECCO 2006. Proceedings of the 8th annual conference on Genetic and evolutionary computation, Seattle, Washington, USA, vol. 1, pp. 941–942. ACM Press, New York (2006)CrossRefGoogle Scholar
  5. 5.
    Leite, J.V., Avila, S.L., Batistela, N.J., Carpes, W.P., Sadowski, N., Kuo-Peng, P., Bastos, J.P.A.: Real coded genetic algorithm for Jiles-Atherton model parameters identification. IEEE Trans. Magn. 40(2), 888–891 (2004)CrossRefGoogle Scholar
  6. 6.
    Cavill, R., Smith, S.L, Tyrrell, A.M: Multi-Chromosomal Genetic Programming. In: GECCO. Proceedings of Genetic and Evolutionary Computation, Washington (2005)Google Scholar
  7. 7.
    Baldonado, M., Chang, C.-C.K., Gravano, L., Paepcke, A.: The Stanford Digital Library Metadata Architecture. Int. J. Digit. Libr. 1, 108–121 (1997)CrossRefGoogle Scholar
  8. 8.
    Mihai, O.: Improving the Search by Encoding Multiple Solutions in a Chromosome. In: Nedjah, N. (ed.) Evolutionary Machine Design, ch. 15, Nova Science Publisher, New-YorkGoogle Scholar
  9. 9.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA (1992)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Hui-yun Quan
    • 1
  • Guangyi Yang
    • 1
  1. 1.College of Mathematics and Computer Science, Hunan Normal University, Changsha 410081, China, Hunan College of Information, Changsha 410200China

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