MREP: Multi-Reference Expression Programming

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9772)

Abstract

MEP is a variant of genetic program applied to solve the symbol regression and classification problems. It can encode multiple solutions of a problem in a single chromosome. However, when the ratio of genes reuse is low, it may not get a high accuracy result within limited iterations and may fall into the trap of local optimum. Therefore, we proposed a novel genetic evolutionary algorithm named MREP (multi-reference expression programming). The MREP chromosome is encoded in a two-dimensional structure and each gene in one chromosome can refer other sub-layer’s gene randomly. The main contribution can be described as follows: Firstly, a novel chromosome encoding scheme is proposed based on a two-dimensional structure. Secondly, two different cross-layer reference strategies are designed to enhance the code reuse of genes located at different layers in one chromosome. Two groups experiments were conducted on eight symbol regression functions. The statistical results reveal that the MREP performs better than the compared algorithms and can solve the symbol regression functions problem efficiently.

Keywords

Multi-expression programming Cross-layer-reference Two dimensional operators Genetic programming Symbol regression 

References

  1. 1.
    Kallel, L., Bart, N., Alex, R.: Theoretical aspects of evolutionary computing. Springer Science & Business Media, New York (2013)Google Scholar
  2. 2.
    Koza, B.J.: Evolving caching algorithms in C by GP. In: Genetic Programming. MIT Press (2010)Google Scholar
  3. 3.
    Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction: On the Automatic Evolution of Computer Programs and Its Applications, December 1998Google Scholar
  4. 4.
    Ryan, C., Neill, M.O.: Grammatical evolution: a steady state approach. Late Breaking Papers Genetic Programming, pp. 180–185 (1998)Google Scholar
  5. 5.
    Ferreira, C.: Gene expression programming: a new adaptive algorithm for solving problems. Eprint Arxiv Cs (2), 87–129 (2001)Google Scholar
  6. 6.
    Miller, J.F.: Gecco 2013 tutorial: Cartesian genetic programming. In: Conference Companion on Genetic and Evolutionary Computation, pp. 715–740 (2013)Google Scholar
  7. 7.
    Paterson, N.R.: Genetic programming with context sensitive grammars. In: Proc. Eurogp Lncs 63(84), 113117 (2002)Google Scholar
  8. 8.
    Oltean, M., Groan, C.: A comparison of several linear genetic programming techniques. Complex Syst. 4, 285–313 (2003)MathSciNetGoogle Scholar
  9. 9.
    Baykasolu, A., Ozbakir, L.: Mepar-miner: multi-expression programming for classification rule mining. Eur. J. Oper. Res. 183(2), 767–784 (2007)CrossRefMATHGoogle Scholar
  10. 10.
    Groan, C., Abraham, A., Ramos, V., Han, S.Y.: Stock market prediction using multi expression programming. In: Portuguese Conference on Artificial Intelligence, EPIA 2005, pp. 73–78 (2006)Google Scholar
  11. 11.
    Oltean, M., Dumitrescu, D.: Evolving TSP heuristics using multi expression programming. In: Bubak, M., Albada, G.D., Sloot, P.M., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3037, pp. 670–673. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Alavi, A.H., Gandomi, A.H., Modaresnezhad, M., Mousavi, M.: New ground-motion prediction equations using multi expression programing. J. Earthquake Eng. 15(4), 511–536 (2011)CrossRefGoogle Scholar
  13. 13.
    Cattani, P.T., Johnson, C.G.: ME-CGP: multi expression Cartesian genetic programming. In: IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 18–23 July, pp. 1–6 (2010)Google Scholar
  14. 14.
    Garg, A., Garg, A., Lam, J.S.L.: Evolving functional expression of permeability of fly ash by a new evolutionary approach. Transport Porous Media 107(2), 555–571 (2015)CrossRefGoogle Scholar
  15. 15.
    Yang, B., Zhang, Q., Wang, L., Li, Y.: Inference of differential equations by MMEP for cement hydration modeling. In: IEEE International Conference on Computer Supported Cooperative Work in Design, pp. 4–10 (2013)Google Scholar
  16. 16.
    Zhang, Q., Yang, B., Wang, L., Jiang, J.: An improved multi-expression programming algorithm applied in function discovery and data prediction. Int. J. Inf. Commun. Technol. 5(5), 218–233 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Qingke Zhang
    • 1
  • Xiangxu Meng
    • 1
  • Bo Yang
    • 2
  • Weiguo Liu
    • 1
  1. 1.School of Computer Science and Technology, Engineering Research Center of Digital Media Technology, Ministry of EducationShandong UniversityJinanChina
  2. 2.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinanChina

Personalised recommendations