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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Publisher: Springer
Languages: English
Types: Part of book or chapter of book
Subjects: Q335
This chapter proposes a generic framework to build geometric dispersion (GD) operators for Geometric Semantic Genetic Programming in the context of symbolic regression, followed by two concrete instantiations of the framework: a multiplicative geometric dispersion operator and an additive geometric dispersion operator. These operators move individuals in the semantic space in order to balance the population around the target output in each dimension, with the objective of expanding the convex hull defined by the population to include the desired output vector. An experimental analysis was conducted in a testbed composed of sixteen datasets showing that dispersion operators can improve GSGP search and that the multiplicative version of the operator is overall better than the additive version.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Albinati, J., Pappa, G.L., Otero, F.E.B., Oliveira, L.O.V.B.: The effect of distinct geometric semantic crossover operators in regression problems. In: Proc. of EuroGP, pp. 3-15 (2015)
    • 2. Banzhaf, W., Nordin, P., Keller, R., Francone, F.: Genetic Programming - an Introduction: on the Automatic Evolution of Computer Programs and Its Applications. Morgan Kaufmann Publishers (1998)
    • 3. Beadle, L., Johnson, C.G.: Semantic analysis of program initialisation in genetic programming. Genetic Prog. and Evolvable Machines 10(3), 307-337 (2009)
    • 4. Botzheim, J., Cabrita, C., Ko´czy, L.T., Ruano, A.E.: Genetic and bacterial programming for b-spline neural networks design. Journal of Advanced Computational Intelligence 11(2), 220- 231 (2007)
    • 5. Castelli, M., Silva, S., Vanneschi, L.: A C++ framework for geometric semantic genetic programming. Genetic Prog. and Evolvable Machines 16(1), 73-81 (2015)
    • 6. Castelli, M., Trujillo, L., Vanneschi, L., Silva, S., Z-Flores, E., Legrand, P.: Geometric semantic genetic programming with local search. In: Proc. GECCO'15, pp. 999-1006. ACM (2015)
    • 7. Castelli, M., Vanneschi, L., Silva, S.: Semantic search-based genetic programming and the effect of intron deletion. Cybernetics, IEEE Trans. on 44(1), 103-113 (2014)
    • 8. Castelli, M., Vanneschi, L., Silva, S., Ruberto, S.: How to exploit alignment in the error space: Two different GP models. In: R. Riolo, et al. (eds.) Genetic Programming Theory and Practice XII, Genetic and Evolutionary Computation, pp. 133-148. Springer International Publishing (2015)
    • 9. Gentle, J.E.: Numerical Linear Algebra for Applications in Statistics. Statistics and Computing. Springer New York (1998)
    • 10. Gonc¸alves, I., Silva, S., Fonseca, C.M.F.: On the generalization ability of geometric semantic genetic programming. In: Proc. of EuroGP, pp. 41-52 (2015)
    • 11. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT Press (1992)
    • 12. Moraglio, A.: Abstract convex evolutionary search. In: Proc. of the 11th FOGA, pp. 151-162 (2011)
    • 13. Moraglio, A., Krawiec, K., Johnson, C.G.: Geometric semantic genetic programming. In: Proc. of PPSN XII, vol. 7491, pp. 21-31. Springer (2012)
    • 14. Ni, J., Drieberg, R.H., Rockett, P.I.: The use of an analytic quotient operator in genetic programming. Evolutionary Computation, IEEE Trans. on 17(1), 146-152 (2013)
    • 15. Oliveira, L.O.V.B., Otero, F.E.B., Pappa, G.L.: A dispersion operator for geometric semantic genetic programming. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference (to appear), GECCO '16. ACM (2016)
    • 16. Pawlak, T.P.: Competent algorithms for geometric semantic genetic programming. Ph.D. thesis, Poznan University of Technology, Poznan, Poland (2015)
    • 17. Pawlak, T.P., Krawiec, K.: Semantic geometric initialization. In: I.M. Heywood, J. McDermott, M. Castelli, E. Costa, K. Sim (eds.) Proc. of the EuroGP'16, LNCS, vol. 9594, pp. 261-277. Springer International Publishing, Cham (2016)
    • 18. Roman, S.: Advanced linear algebra, Graduate Texts in Mathematics, vol. 135, 2nd edn. Springer New York (2005)
    • 19. Ruberto, S., Vanneschi, L., Castelli, M., Silva, S.: ESAGP - a semantic GP framework based on alignment in the error space. In: Proc. of EuroGP, pp. 150-161 (2014)
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