Abstract
This paper investigates the impact of geometric semantic crossover operators in a wide range of symbolic regression problems. First, it analyses the impact of using Manhattan and Euclidean distance geometric semantic crossovers in the learning process. Then, it proposes two strategies to numerically optimize the crossover mask based on mathematical properties of these operators, instead of simply generating them randomly. An experimental analysis comparing geometric semantic crossovers using Euclidean and Manhattan distances and the proposed strategies is performed in a test bed of twenty datasets. The results show that the use of different distance functions in the semantic geometric crossover has little impact on the test error, and that our optimized crossover masks yield slightly better results. For SGP practitioners, we suggest the use of the semantic crossover based on the Euclidean distance, as it achieved similar results to those obtained by more complex operators.
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References
Angeline, P.J.: Subtree crossover: building block engine or macromutation. Genet. Program. 97, 9–17 (1997)
Arnaldo, I., Krawiec, K., O’Reilly, U.M.: Multiple regression genetic programming. In: Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, pp. 879–886. ACM (2014)
Beadle, L., Johnson, C.G.: Semantically driven crossover in genetic programming. In: IEEE World Congress on Computational Intelligence, pp. 111–116 (2008)
Beadle, L., Johnson, C.G.: Semantic analysis of program initialisation in genetic programming. Genet. Program. Evolvable Mach. 10(3), 307–337 (2009)
Castelli, M., Silva, S., Vanneschi, L.: A C++ framework for geometric semantic genetic programming. Genetic Programming and Evolvable Machines (2014). http://dx.doi.org/10.1007/s10710-014-9218-0
Krawiec, K., Lichocki, P.: Approximating geometric crossover in semantic space. In: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pp. 987–994. ACM (2009)
McDermott, J., White, D.R., Luke, S., Manzoni, L., Castelli, M., Vanneschi, L., Jaskowski, W., Krawiec, K., Harper, R., De Jong, K., et al.: Genetic programming needs better benchmarks. In: Proceedings of the Fourteenth International Genetic and Evolutionary Computation Conference, pp. 791–798. ACM (2012)
Moraglio, A., Krawiec, K., Johnson, C.G.: Geometric semantic genetic programming. In: Coello Coello, C.A., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN XII. LNCS, vol. 7491, pp. 21–31. Springer, Heidelberg (2012)
Uy, N.Q., Hoai, N.X., O’Neill, M., McKay, R.I., Galván-López, E.: Semantically-based crossover in genetic programming: application to real-valued symbolic regression. Genet. Program. Evolvable Mach. 12(2), 91–119 (2011)
Uy, N.Q., O’Neill, M., Hoai, N.X., McKay, B., Galván-López, E.: Semantic similarity based crossover in GP: the case for real-valued function regression. In: Collet, P., Monmarcé, N., Legrand, P., Schoenauer, M., Lutton, E. (eds.) Artifical Evolution. LNCS, vol. 5975, pp. 170–181. Springer, Heidelberg (2010)
Vanneschi, L., Castelli, M., Manzoni, L., Silva, S.: A new implementation of geometric semantic GP and its application to problems in pharmacokinetics. In: Krawiec, K., Moraglio, A., Hu, T., Şima Etaner-Uyar, A. (eds.) Genetic Programming. LNCS, vol. 7831, pp. 205–216. Springer, Heidelberg (2013)
Vanneschi, L., Castelli, M., Silva, S.: A survey of semantic methods in genetic programming. Genet. Program. Evolvable Mach. 15(2), 195–214 (2014)
Vanneschi, L., Castelli, M., Manzoni, L., Silva, S.: A new implementation of geometric semantic GP and its application to problems in pharmacokinetics. In: Krawiec, K., Moraglio, A., Hu, T., Şima Etaner-Uyar, A., Hu, B. (eds.) Genetic Programming. LNCS, vol. 7831. Springer, Heidelberg (2014)
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Albinati, J., Pappa, G.L., Otero, F.E.B., Oliveira, L.V.B. (2015). The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems. In: Machado, P., et al. Genetic Programming. EuroGP 2015. Lecture Notes in Computer Science(), vol 9025. Springer, Cham. https://doi.org/10.1007/978-3-319-16501-1_1
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DOI: https://doi.org/10.1007/978-3-319-16501-1_1
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