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Tournament Selection Based on Statistical Test in Genetic Programming

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9921)

Abstract

Selection plays a critical role in the performance of evolutionary algorithms. Tournament selection is often considered the most popular techniques among several selection methods. Standard tournament selection randomly selects several individuals from the population and the individual with the best fitness value is chosen as the winner. In the context of Genetic Programming, this approach ignores the error value on the fitness cases of the problem emphasising relative fitness quality rather than detailed quantitative comparison. Subsequently, potentially useful information from the error vector may be lost. In this paper, we introduce the use of a statistical test into selection that utilizes information from the individual’s error vector. Two variants of tournament selection are proposed, and tested on Genetic Programming for symbolic regression problems. On the benchmark problems examined we observe a benefit of the proposed methods in reducing code growth and generalisation error.

Keywords

  • Genetic Programming
  • Tournament selection
  • Statistical test

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References

  1. Altenberg, L.: The evolution of evolvability in genetic programming. In: Advances in Genetic Programming, pp. 47–74. MIT Press (1994)

    Google Scholar 

  2. Bache, K., Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml

  3. Bäck, T.: Selective pressure in evolutionary algorithms: a characterization of selection mechanisms. In: Proceedings of the First IEEE Conference on Evolutionary Computation, pp. 57–62. IEEE Press, Piscataway (1994)

    Google Scholar 

  4. Blickle, T., Thiele, L.: A comparison of selection schemes used in evolutionary algorithms. Evol. Comput. 4(4), 361–394 (1996)

    CrossRef  Google Scholar 

  5. Cumming, G.: Understanding The New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. Routledge, New York (2012)

    Google Scholar 

  6. Fang, Y., Li, J.: A review of tournament selection in genetic programming. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds.) ISICA 2010. LNCS, vol. 6382, pp. 181–192. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  7. Gathercole, C.: An investigation of supervised learning in genetic programming. Ph.D. thesis. University of Edinburgh (1998)

    Google Scholar 

  8. Jong, E.D.D., Pollack, J.B.: Multi-objective methods for tree size control. Genet. Program. Evolvable Mach. 4(3), 211–233 (2003)

    CrossRef  Google Scholar 

  9. Kim, J.J., Zhang, B.T.: Effects of selection schemes in genetic programming for time series prediction. Proc. Congr. Evol. Comput. 1, 252–258 (1999)

    Google Scholar 

  10. Nguyen, Q.U., Nguyen, X.H., O’Neill, M., McKay, R.I., Galvan-Lopez, E.: Semantically-based crossover in genetic programming: application to real-valued symbolic regression. Genet. Program. Evolvable Mach. 12(2), 91–119 (2011)

    CrossRef  Google Scholar 

  11. Nguyen, Q.U., Pham, T.A., Nguyen, X.H., McDermott, J.: Subtree semantic geometric crossover for genetic programming. Genet. Program. Evolvable Mach. 17(1), 25–53 (2016)

    CrossRef  Google Scholar 

  12. Pawlak, T.P., Wieloch, B., Krawiec, K.: Review and comparative analysis of geometric semantic crossovers. Genet. Program. Evolvable Mach. 16(3), 351–386 (2015)

    CrossRef  Google Scholar 

  13. Pawlak, T.P., Wieloch, B., Krawiec, K.: Semantic backpropagation for designing search operators in genetic programming. IEEE Trans. Evol. Comput. 19(3), 326–340 (2015)

    CrossRef  Google Scholar 

  14. Silva, S., Dignum, S., Vanneschi, L.: Operator equalisation for bloat free genetic programming and a survey of bloat control methods. Genet. Program. Evolvable Mach. 13(2), 197–238 (2012)

    CrossRef  Google Scholar 

  15. Sokolov, A., Whitley, D.: Unbiased tournament selection. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pp. 1131–1138. ACM, New York (2005)

    Google Scholar 

  16. White, D.R., McDermott, J., Castelli, M., Manzoni, L., Goldman, B.W., Kronberger, G., Jaskowski, W., O’Reilly, U.M., Luke, S.: Better GP benchmarks: community survey results and proposals. Genet. Program. Evolvable Mach. 14(1), 3–29 (2013)

    CrossRef  Google Scholar 

  17. Xie, H., Zhang, M.: Parent selection pressure auto-tuning for tournament selection in genetic programming. IEEE Trans. Evol. Comput. 17(1), 1–19 (2013)

    CrossRef  Google Scholar 

  18. Xie, H., Zhang, M., Andreae, P., Johnston, M.: Is the not-sampled issue in tournament selection critical? In: IEEE World Congress on Computational Intelligence, pp. 3710–3717, June 2008

    Google Scholar 

  19. Xie, H., Zhang, M., Andreae, P.: Automatic selection pressure control in genetic programming. In: Yang, B., Chen, Y. (eds.) 6th International Conference on Intelligent System Design and Applications, pp. 435–440. IEEE (2006)

    Google Scholar 

  20. Xie, H., Zhang, M., Andreae, P., Johnson, M.: An analysis of multi-sampled issue and no-replacement tournament selection. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, GECCO 2008, pp. 1323–1330. ACM, New York (2008)

    Google Scholar 

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Acknowledgment

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.01-2014.09. MON acknowledges the support of Science Foundation Ireland grant 13/IA/1850.

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Correspondence to Quang Uy Nguyen .

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Chu, T.H., Nguyen, Q.U., O’Neill, M. (2016). Tournament Selection Based on Statistical Test in Genetic Programming. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_28

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  • DOI: https://doi.org/10.1007/978-3-319-45823-6_28

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