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.
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References
Altenberg, L.: The evolution of evolvability in genetic programming. In: Advances in Genetic Programming, pp. 47–74. MIT Press (1994)
Bache, K., Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
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)
Blickle, T., Thiele, L.: A comparison of selection schemes used in evolutionary algorithms. Evol. Comput. 4(4), 361–394 (1996)
Cumming, G.: Understanding The New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. Routledge, New York (2012)
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)
Gathercole, C.: An investigation of supervised learning in genetic programming. Ph.D. thesis. University of Edinburgh (1998)
Jong, E.D.D., Pollack, J.B.: Multi-objective methods for tree size control. Genet. Program. Evolvable Mach. 4(3), 211–233 (2003)
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)
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)
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)
Pawlak, T.P., Wieloch, B., Krawiec, K.: Review and comparative analysis of geometric semantic crossovers. Genet. Program. Evolvable Mach. 16(3), 351–386 (2015)
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)
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)
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)
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)
Xie, H., Zhang, M.: Parent selection pressure auto-tuning for tournament selection in genetic programming. IEEE Trans. Evol. Comput. 17(1), 1–19 (2013)
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
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)
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)
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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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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