Science China Information Sciences

, Volume 54, Issue 12, pp 2544–2553 | Cite as

Modeling grammatical evolution by automaton

  • Pei HeEmail author
  • Colin G. Johnson
  • HouFeng Wang
Research Papers


Twelve years have passed since the advent of grammatical evolution (GE) in 1998, but such issues as vast search space, genotypic readability, and the inherent relationship among grammatical concepts, production rules and derivations have remained untouched in almost all existing GE researches. Model-based approach is an attractive method to achieve different objectives of software engineering. In this paper, we make the first attempt to model syntactically usable information of GE using an automaton, coming up with a novel solution called model-based grammatical evolution (MGE) to these problems. In MGE, the search space is reduced dramatically through the use of concepts from building blocks, but the functionality and expressiveness are still the same as that of classical GE. Besides, complex evolutionary process can visually be analyzed in the context of transition diagrams.


genetic programming grammatical evolution finite state automaton model 


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  1. 1.
    O’Neill M, Ryan C. Grammatical evolution. IEEE Trans Evolut Comput, 2001, 5: 349–358CrossRefGoogle Scholar
  2. 2.
    Ryan C, Collins J J, O’Neill M. Grammatical evolution: Evolving programs for an arbitrary language. In: Banzhaf W, Poli R, Schoenauer M, et al., eds. Proc of the First European Workshop on Genetic Programming (EuroGP98), LNCS, 1998, 1391: 83–96Google Scholar
  3. 3.
    O’Neill M, Ryan C. Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Norwell, MA: Kluwer Academic Publishers, 2003zbMATHGoogle Scholar
  4. 4.
    Mitchell M. An Introduction to Genetic Algorithms. Cambridge: MIT Press, 1996Google Scholar
  5. 5.
    Hopcroft J E, Motwani R, Ullman J D. Introduction to Automata Theory, Languages, and Computation. 3rd ed. San Antonio, TX: Pearson Education, Inc. 2008Google Scholar
  6. 6.
    Aho A V, Lam M S, Sethi R, et al. Compilers: Principles, Techniques, and Tools. 2nd ed. San Antonio, TX: Pearson Education, Inc. 2007Google Scholar
  7. 7.
    Koza J R. Genetic Programming. Cambridge MA: MIT Press, 1992zbMATHGoogle Scholar
  8. 8.
    Oltean M, Grosan C. A comparison of several linear genetic programming techniques. Complex Syst, 2003, 14: 285–313MathSciNetGoogle Scholar
  9. 9.
    Sette S, Boullart L. Genetic programming: principles and applications. Eng Appl Artif Intell, 2001, 14: 727–736CrossRefGoogle Scholar
  10. 10.
    Gavrilis D, Tsoulos I G, Dermatas E. Selecting and constructing features using grammatical evolution. Patt Recog Lett, 2008, 29: 1358–1365CrossRefGoogle Scholar
  11. 11.
    Tsoulos I G, Gavrilis D, Glavas E. Neural network construction and training using grammatical evolution. Neurocomputing, 2008. 72: 269–277CrossRefGoogle Scholar
  12. 12.
    Dempsey I, O’Neill M, Brabazon A. Adaptive trading with grammatical evolution. In: Proc of 2006 IEEE Congress on Evolutionary Computation. Vancouver, BC, Canada, 2006. 2587–2592Google Scholar
  13. 13.
    Tsoulos I G, Gavrilis D, Dermatas E. GDF: A tool for function estimation through grammatical evolution. Comput Phys Commun, 2006, 174: 555–559CrossRefGoogle Scholar
  14. 14.
    Ferreira C. Gene expression programming: A new adaptive algorithm for solving problems. Complex Syst, 2001, 13: 87–129zbMATHGoogle Scholar
  15. 15.
    Xu K K, Liu Y T, Tang R, et al. A novel method for real parameter optimization based on gene expression programming. Appl Soft Comput, 2009, 9: 725–737CrossRefGoogle Scholar
  16. 16.
    Du X, Ding L X. About the convergence rates of a class of gene expression programming. Sci China Inf Sci, 2010, 53: 715–728CrossRefMathSciNetGoogle Scholar
  17. 17.
    Pierce B C. Types and Programming Languages. Cambridge, MA: The MIT Press, 2002Google Scholar
  18. 18.
    Boolos G S, Burgess J P, Jeffrey R C. Computability and Logic. 4th ed. Cambridge: Cambridge Univ. Press, 2002zbMATHGoogle Scholar
  19. 19.
    Wong M L, Mun T. Evolving recursive programs by using adaptive grammar based genetic programming. Genetic Program Evolv Mach, 2005, 6: 421–455CrossRefGoogle Scholar
  20. 20.
    Wilson D, Kaur D. Search, neutral evolution, and mapping in evolutionary computing: A case study of grammatical evolution. IEEE Trans Evolut Comput, 2009, 13: 566–590CrossRefGoogle Scholar
  21. 21.
    He P, Kang L S, Fu M. Formality based genetic programming. In: IEEE Congress on Evolutionary Computation. Hong Kong, 2008Google Scholar
  22. 22.
    He P, Kang L S, Johnson C G, et al. Hoare logic-based genetic programming. Sci China Inf Sci, 2011, 54: 623–637CrossRefzbMATHMathSciNetGoogle Scholar
  23. 23.
    Harman M, Mansouri S A, Zhang Y Y. Search based software engineering: A comprehensive analysis and review of trends techniques and application. Technical Report, TR-09-03, 2009Google Scholar
  24. 24.
    Hugosson J, Hemberg E, Brabazon A, et al. Genotype representations in grammatical evolution. Appl Soft Comput, 2010, 10: 36–43CrossRefGoogle Scholar
  25. 25.
    O’Neill M, Brabazon A, Nicolau M, et al. πGrammatical evolution. In: Deb K, Poli R, Banzhaf W, et al. eds. Proc. GECCO, LNCS, 2004, 3103: 617–629Google Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.State Key Laboratory of Software EngineeringWuhan UniversityWuhanChina
  2. 2.School of ComputingUniversity of KentCanterburyEngland
  3. 3.School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangshaChina
  4. 4.Institute of Computational LinguisticsPeking UniversityBeijingChina

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