EDDA-V2 – An Improvement of the Evolutionary Demes Despeciation Algorithm

  • Illya Bakurov
  • Leonardo Vanneschi
  • Mauro Castelli
  • Francesco Fontanella
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11101)


For any population-based algorithm, the initialization of the population is a very important step. In Genetic Programming (GP), in particular, initialization is known to play a crucial role - traditionally, a wide variety of trees of various sizes and shapes are desirable. In this paper, we propose an advancement of a previously conceived Evolutionary Demes Despeciation Algorithm (EDDA), inspired by the biological phenomenon of demes despeciation. In the pioneer design of EDDA, the initial population is generated using the best individuals obtained from a set of independent subpopulations (demes), which are evolved for a few generations, by means of conceptually different evolutionary algorithms - some use standard syntax-based GP and others use a semantics-based GP system. The new technique we propose here (EDDA-V2), imposes more diverse evolutionary conditions - each deme evolves using a distinct random sample of training data instances and input features. Experimental results show that EDDA-V2 is a feasible initialization technique: populations converge towards solutions with comparable or even better generalization ability with respect to the ones initialized with EDDA, by using significantly reduced computational time.


Initialization algorithm Semantics Despeciation 


  1. 1.
    Archetti, F., Lanzeni, S., Messina, E., Vanneschi, L.: Genetic programming for computational pharmacokinetics in drug discovery and development. Genet. Program. Evol. Mach. 8(4), 413–432 (2007)CrossRefGoogle Scholar
  2. 2.
    Beadle, L.C.J.: Semantic and structural analysis of genetic programming. Ph.D. thesis, University of Kent, Canterbury, July 2009Google Scholar
  3. 3.
    Beadle, L.C.J., Johnson, C.G.: Semantic analysis of program initialisation in genetic programming. Genet. Program. Evol. Mach. 10(3), 307–337 (2009)CrossRefGoogle Scholar
  4. 4.
    Castelli, M., Manzoni, L., Vanneschi, L., Silva, S., Popovič, A.: Self-tuning geometric semantic genetic programming. Genet. Program. Evol. Mach. 17(1), 55–74 (2016)CrossRefGoogle Scholar
  5. 5.
    Castelli, M., Silva, S., Vanneschi, L.: A C++ framework for geometric semantic genetic programming. Genet. Program. Evol. Mach. 16(1), 73–81 (2015)CrossRefGoogle Scholar
  6. 6.
    Castelli, M., Vanneschi, L., Felice, M.D.: Forecasting short-term electricity consumption using a semantics-based genetic programming framework: the south italy case. Energy Econ. 47, 37–41 (2015)CrossRefGoogle Scholar
  7. 7.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  8. 8.
    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). Scholar
  9. 9.
    Oliveira, L.O.V., Otero, F.E., Pappa, G.L.: A dispersion operator for geometric semantic genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, GECCO 2016, pp. 773–780. ACM (2016)Google Scholar
  10. 10.
    Pawlak, T.P., Wieloch, B., Krawiec, K.: Review and comparative analysis of geometric semantic crossovers. Genet. Program. Evol. Mach. 16(3), 351–386 (2015)CrossRefGoogle Scholar
  11. 11.
    Taylor, E.B., Boughman, J.W., Groenenboom, M., Sniatynski, M., Schluter, D., Gow, J.L.: Speciation in reverse: morphological and genetic evidence of the collapse of a three-spined stickleback (gasterosteus aculeatus) species pair. Mol. Ecol. 15(2), 343–355 (2006)CrossRefGoogle Scholar
  12. 12.
    Tomassini, M., Vanneschi, L., Collard, P., Clergue, M.: A study of fitness distance correlation as a difficulty measure in genetic programming. Evol. Comput. 13(2), 213–239 (2005)CrossRefGoogle Scholar
  13. 13.
    Vanneschi, L.: An introduction to geometric semantic genetic programming. In: Schütze, O., Trujillo, L., Legrand, P., Maldonado, Y. (eds.) NEO 2015. SCI, vol. 663, pp. 3–42. Springer, Cham (2017). Scholar
  14. 14.
    Vanneschi, L., Bakurov, I., Castelli, M.: An initialization technique for geometric semantic GP based on demes evolution and despeciation. In: 2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebastián, Spain, 5–8 June 2017, pp. 113–120 (2017)Google Scholar
  15. 15.
    Vanneschi, L., Castelli, M., Silva, S.: A survey of semantic methods in genetic programming. Genet. Program. Evol. Mach. 15(2), 195–214 (2014)CrossRefGoogle Scholar
  16. 16.
    Vanneschi, L., Silva, S., Castelli, M., Manzoni, L.: Geometric semantic genetic programming for real life applications. In: Riolo, R., Moore, J.H., Kotanchek, M. (eds.) Genetic Programming Theory and Practice XI. GEC, pp. 191–209. Springer, New York (2014). Scholar
  17. 17.
    Wilson, D.S.: Structured demes and the evolution of group-advantageous traits. Am. Nat. 111(977), 157–185 (1977). Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Illya Bakurov
    • 1
  • Leonardo Vanneschi
    • 1
  • Mauro Castelli
    • 1
  • Francesco Fontanella
    • 2
  1. 1.NOVA Information Management School (NOVA IMS)Universidade Nova de LisboaLisbonPortugal
  2. 2.Dipartimento di Ingegneria Elettrica e dell’Informazione (DIEI)Università di Cassino e del Lazio MeridionaleCassinoItaly

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