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Geometric Semantic Genetic Programming with Perpendicular Crossover and Random Segment Mutation for Symbolic Regression

  • Qi ChenEmail author
  • Mengjie Zhang
  • Bing Xue
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10593)

Abstract

Geometric semantic operators have been a rising topic in genetic programming (GP). For the sake of a more effective evolutionary process, various geometric search operators have been developed to utilise the knowledge acquired from inspecting the behaviours of GP individuals. While the current exact geometric operators lead to over-grown offsprings in GP, existing approximate geometric operators never consider the theoretical framework of geometric semantic GP explicitly. This work proposes two new geometric search operators, i.e. perpendicular crossover and random segment mutation, to fulfil precise semantic requirements for symbolic regression under the theoretical framework of geometric semantic GP. The two operators approximate the target semantics gradually and effectively. The results show that the new geometric operators bring a notable benefit to both the learning performance and the generalisation ability of GP. In addition, they also have significant advantages over Random Desired Operator, which is a state-of-the-art geometric semantic operator.

Keywords

Genetic programming Symbolic regression Geometric semantic operators 

References

  1. 1.
    Archetti, F., Lanzeni, S., Messina, E., Vanneschi, L.: Genetic programming for computational pharmacokinetics in drug discovery and development. Genetic Program. Evol. Mach. 8(4), 413–432 (2007)CrossRefGoogle Scholar
  2. 2.
    Chen, Q., Xue, B., Mei, Y., Zhang, M.: Geometric semantic crossover with an angle-aware mating scheme in genetic programming for symbolic regression. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds.) EuroGP 2017. LNCS, vol. 10196, pp. 229–245. Springer, Cham (2017). doi: 10.1007/978-3-319-55696-3_15 CrossRefGoogle Scholar
  3. 3.
    Fortin, F.A., Rainville, F.M.D., Gardner, M.A., Parizeau, M., Gagné, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Gonçalves, I., Silva, S., Fonseca, C.M.: On the generalization ability of geometric semantic genetic programming. In: Machado, P., Heywood, M.I., McDermott, J., Castelli, M., García-Sánchez, P., Burelli, P., Risi, S., Sim, K. (eds.) EuroGP 2015. LNCS, vol. 9025, pp. 41–52. Springer, Cham (2015). doi: 10.1007/978-3-319-16501-1_4 Google Scholar
  5. 5.
    Koza, J.R.: Genetic Programming: on the Programming of Computers by Means of Natural Selection, vol. 1. MIT Press, Cambridge (1992)Google Scholar
  6. 6.
    Krawiec, K., O’Reilly, U.M.: Behavioral programming: a broader and more detailed take on semantic GP. In: Proceedings of the 16th Annual Conference on Genetic and Evolutionary Computation Conference (GECCO), pp. 935–942 (2014)Google Scholar
  7. 7.
    Moraglio, A., Krawiec, K., Johnson, C.G.: Geometric semantic genetic programming. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012. LNCS, vol. 7491, pp. 21–31. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-32937-1_3 CrossRefGoogle Scholar
  8. 8.
    Nguyen, Q.U., Nguyen, X.H., O’Neill, M.: Semantic aware crossover for genetic programming: the case for real-valued function regression. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds.) EuroGP 2009. LNCS, vol. 5481, pp. 292–302. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-01181-8_25 CrossRefGoogle Scholar
  9. 9.
    Pawlak, T.P.: Geometric semantic genetic programming is overkill. In: Heywood, M.I., McDermott, J., Castelli, M., Costa, E., Sim, K. (eds.) EuroGP 2016. LNCS, vol. 9594, pp. 246–260. Springer, Cham (2016). doi: 10.1007/978-3-319-30668-1_16 CrossRefGoogle Scholar
  10. 10.
    Pawlak, T.P., Krawiec, K.: Guarantees of progress for geometric semantic genetic programming. In: Semantic Methods in Genetic Programming, Ljubljana, Slovenia, vol. 13 (2014)Google Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Rosenwald, A., Wright, G., Chan, W.C., Connors, J.M., Campo, E., Fisher, R.I., Gascoyne, R.D., Muller-Hermelink, H.K., Smeland, E.B., Giltnane, J.M., et al.: The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N. Engl. J. Med. 346(25), 1937–1947 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

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