Skip to main content
SpringerLink
Log in

An Analysis of Semantic Aware Crossover

  • Conference paper
Computational Intelligence and Intelligent Systems (ISICA 2009)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 51))

Included in the following conference series:

Abstract

It is well-known that the crossover operator plays an important role in Genetic Programming (GP). In Standard Crossover (SC), semantics are not used to guide the selection of the crossover points, which are generated randomly. This lack of semantic information is the main cause of destructive effects from SC (e.g., children having lower fitness than their parents). Recently, we proposed a new semantic based crossover known GP called Semantic Aware Crossover (SAC) [25]. We show that SAC outperforms SC in solving a class of real-value symbolic regression problems. We clarify the effect of SAC on GP search in increasing the semantic diversity of the population, thus helping to reduce the destructive effects of crossover in GP.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Beadle, L., Johnson, C.: Semantically driven crossover in genetic programming. In: Proc. IEEE WCCI 2008, pp. 111–116. IEEE Press, Los Alamitos (2008)

    Google Scholar 

  2. Burke, E.K., Gustafson, S., Kendall, G.: Diversity in genetic programming: An analysis of measures and correlation with fitness. IEEE Transactions on Evolutionary Computation 8(1), 47–62 (2004)

    Article  Google Scholar 

  3. Cleary, R., O’Neill, M.: An attribute grammar decoder for the 01 multi-constrained knapsack problem. In: Raidl, G.R., Gottlieb, J. (eds.) EvoCOP 2005. LNCS, vol. 3448, pp. 34–45. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  4. de la Cruz Echeand’a, M., de la Puente, A.O., Alfonseca, M.: Attribute grammar evolution. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 182–191. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  5. Ghodrat, M.A., Givargis, T., Nicolau, A.: Equivalence checking of arithmetic expressions using fast evaluation. In: Proc. of the International Conference on Compilers, Architecture and Synthesis for Embedded Systems – CASES 2005. ACM, New York (2005)

    Google Scholar 

  6. Hengpraprohm, S., Chongstitvatana, P.: Selective crossover in genetic programming. In: Proc. of ISCIT International Symposium on Communications and Information Technologies, November 2001, pp. 14–16 (2001)

    Google Scholar 

  7. Hien, N.T., Hoai, N.X.: A brief overview of population diversity measures in genetic programming. In: Proc. of 11th Asia-Pacific Workshop on Intelligent and Evolutionary Systems, October 2006, pp. 128–139. Vietnamese Military Technical Academy (2006)

    Google Scholar 

  8. Hoai, N.X., McKay, R., Essam, D.: Solving the symbolic regression problem with tree-adjunct grammar guided genetic programming: The comparative results. In: Proc. of CEC 2002, pp. 1326–1331. IEEE Press, Los Alamitos (2002)

    Google Scholar 

  9. Ito, T., Iba, H., Sato, S.: Depth-dependent crossover for genetic programming. In: Proc. of IEEE WCCI 1998, pp. 775–780. IEEE Press, Los Alamitos (1998)

    Google Scholar 

  10. Johnson, C.: Deriving genetic programming fitness properties by static analysis. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A.G.B. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 298–308. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  11. Johnson, C.: What can automatic programming learn from theoretical computer science. In: Proc. of the UK Workshop on Computational Intelligence. University of Birmingham (2002)

    Google Scholar 

  12. Johnson, C.: Genetic programming with fitness based on model checking. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 114–124. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  13. Katz, G., Peled, D.: Genetic programming and model checking: Synthesizing new mutual exclusion algorithms. In: Cha, S(S.), Choi, J.-Y., Kim, M., Lee, I., Viswanathan, M. (eds.) ATVA 2008. LNCS, vol. 5311, pp. 33–47. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  14. Katz, G., Peled, D.: Model checking-based genetic programming with an application to mutual exclusion. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 141–156. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  15. Keijzer, M.: Alternatives in subtree caching for genetic programming. In: Keijzer, M., O’Reilly, U.-M., Lucas, S., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 328–337. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  16. Koza, J.: Genetic Programming: On the Programming of Computers by Natural Selection. MIT Press, MA (1992)

    MATH  Google Scholar 

  17. Majeed, H., Ryan, C.: A less destructive, context-aware crossover operator for GP. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 36–48. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  18. Majeed, H., Ryan, C.: On the constructiveness of context-aware crossover. In: Proc. of GECCO 2007, pp. 1659–1666. ACM Press, New York (2007)

    Chapter  Google Scholar 

  19. McPhee, N., Ohs, B., Hutchison, T.: Semantic building blocks in genetic programming. In: O’Neill, M., Vanneschi, L., Gustafson, S., Esparcia Alcázar, A.I., De Falco, I., Della Cioppa, A., Tarantino, E. (eds.) EuroGP 2008. LNCS, vol. 4971, pp. 134–145. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  20. Mori, N.: A novel diversity measure of genetic programming. In: Proc. of Randomness and Computation: Joint Workshop “New Horizons in Computing” and “Statistical Mechanical Approach to Probabilistic Information, July 2005, pp. 18–21 (2005)

    Google Scholar 

  21. Mori, N., McKay, R., Hoai, N.X., Essam, D.: Equivalent decision simplification: A new method for simplifying algebraic expressions in genetic programming. In: Proc. of 11th Asia-Pacific Workshop on Intelligent and Evolutionary Systems (2007)

    Google Scholar 

  22. O’Reilly, U.M.: Using a distance metric on genetic programs to understand genetic operators. In: Proc. of IEEE International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation, pp. 4092–4097. IEEE, Los Alamitos (1997)

    Chapter  Google Scholar 

  23. Poli, R., Langdon, W., McPhee, N.: A Field Guide to Genetic Programming (2008), http://lulu.com

  24. Poli, R., Langdon, W.B.: Genetic programming with one-point crossover. In: Proc. of Soft Computing in Engineering Design and Manufacturing Conference, pp. 180–189. Springer, Heidelberg (1997)

    Google Scholar 

  25. Uy, N.Q., Hoai, N.X., O’Neill, M.: Semantic aware crossover for genetic programming: the case for real-valued function regression. In: Vanneschi, L., et al. (eds.) EuroGP 2009. LNCS, vol. 5481, pp. 25–36. Springer, Heidelberg (2009)

    Google Scholar 

  26. Wong, M.L., Leung, K.S.: An induction system that learns programs in different programming languages using genetic programming and logic grammars. In: Proc.  of the 7th IEEE International Conference on Tools with Artificial Intelligence (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Natural Computing Research & Applications Group, University College Dublin, Ireland

    Nguyen Quang Uy, Michael O’Neill & Edgar Galván-López

  2. School of Computer Science and Engineering, Seoul National University, Korea

    Nguyen Xuan Hoai & Bob McKay

Authors
  1. Nguyen Quang Uy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nguyen Xuan Hoai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael O’Neill
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bob McKay
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Edgar Galván-López
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computer Science, China University of Geosciences, 430074, Wu-Han, China

    Zhihua Cai

  2. State Key Laboratory of Novel Software Technology,, Nanjing University, P.O. Box, China

    Zhenhua Li

  3. Computation Center, Wuhan University, Wuhan, China

    Zhuo Kang

  4. School of Computer Science and Engineering, The University of Aizu, Japan

    Yong Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Uy, N.Q., Hoai, N.X., O’Neill, M., McKay, B., Galván-López, E. (2009). An Analysis of Semantic Aware Crossover. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2009. Communications in Computer and Information Science, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04962-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04962-0_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04961-3

  • Online ISBN: 978-3-642-04962-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation