Tree-Structure-Aware Genetic Operators in Genetic Programming

  • Seo, Kisung (Dept. of Electronics Engineering, Seokyeong University) ;
  • Pang, Chulhyuk (Green Energy Division, AP Systems)
  • Received : 2013.07.27
  • Accepted : 2013.11.11
  • Published : 2014.03.01


In this paper, we suggest tree-structure-aware GP (Genetic Programming) operators that heed tree distributions in structure space and their possible structural difficulties. The main idea of the proposed GP operators is to place the generated offspring of crossover and/or mutation in a specified region of tree structure space insofar as possible by biasing the tree structures of the altered subtrees, taking into account the observation that most solutions are found in that region. To demonstrate the effectiveness of the proposed approach, experiments on the binomial-3 regression, multiplexor and even parity problems are performed. The results show that the results using the proposed tree-structure-aware operators are superior to the results of standard GP for all three test problems in both success rate and number of evaluations.


Genetic programming;Genetic operators;Crossover;Balanced tree structures


  1. J. R. Koza, Genetic Programming: On the Programming of Computers by Natural Selection, MIT Press, Cambridge, MA, USA, 1992
  2. S. Luke, Issues in Scaling Genetic Programming Breeding Strategies, Tree Generation and Code Bloat, PhD thesis, University of Maryland, 2000
  3. D. Goldberg, Genetic algorithms in search, optimization, and machine learning, Addison-Wesley; 1989.
  4. J.W. Davidson, D.A. Savic, G.A. Walters, "Symbolic and numerical regression: experiments and applications," Information Sciences, Volume 150, Issues 1-2, March 2003, pp. 95-117
  5. K. Seo S. Hyun E. D. Goodman, "Genetic Programming- Based Automatic Gait Generation in Joint Space for a Quadruped Robot," Advanced Robotics, Vol. 24, No. 15, pp. 2199-2214. 2010.
  6. K. Seo, B. Hyeon, S. Hyun, and Y. Lee, "Genetic Programming- Based Model Output Statistics for Short- Range Temperature Prediction," Lecture Notes in Computer Science, Springer-Verlag, Vol. 7835, pp. 122-131, 2013
  7. H. Majeed, C. Ryan, "On the Constructiveness of Context Aware Crossover," Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'07), pp.1659-1666, London, England, United Kingdom, July 7-11, 2007
  8. R. Poli, J. Page, "Solving High Order Boolean Parity Problems with Smooth Uniform Crossover," Sub Machine Code GP and Demes, Genetic Programming and Evolvable Machines, 1(1/2), pp. 37-56, April 2000
  9. T. Ito, H. Iba, S. Sato, "Depth Dependent Crossover for Genetic Programming," in Proceedings of the World Congress on Computational Intelligence, pp. 775-780, Anchorage, AK. USA, May 4-9, 1998
  10. J. M. Daida, A. M. Hilss, "Identifying Structural Mechanisms in Standard Genetic Programming," Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2003), LNCS 2724, pp.1639-1651, Chicago, IL, USA, July 12-16, 2003.
  11. J. M. Daida, J. A. Polito, S. A. Stanhope, R. R. Berttam, J. C. Khoo, "What Makes a Problem GP Hard? Analysis of a Tunably Difficult Problem in Genetic Programming," Genetic Programming and Evolvable Machines, ISSN 1389-2576, 2(2), June 2001, pp.165-191.
  12. Ngyen, X. Hoai, B. McKay, D. Essam, "Representation and structural Difficulty in Genetic Programming," IEEE Transactions on Evolutionary Computation, 10(2), pp.157-166, April 2006
  13. D. Zongker, B. Punch, Lil-GP User's Manual, Michigan State University, July, 1995
  14. S. Sliva, E. Costa, "Resource Limited Genetic Programming: The Dynamic Approach," Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'05), pp.1673-1680, Washington, DC, USA, June 25-29, 2005