Genetic Programming and Evolvable Machines

, Volume 15, Issue 1, pp 37–60 | Cite as

GP-induced and explicit bloating of the seeds in incremental GP improves evolutionary success

  • Ivan Tanev
  • Tüze Kuyucu
  • Katsunori Shimohara


The parsimony control in genetic programming (GP) is one of the limiting factors in the quick evolution of efficient solutions. A variety of parsimony pressure methods have been developed to address this issue. The effects of these methods on the efficiency of evolution are recognized to depend on the characteristics of the applied problem domain. On the other hand, the implications of using parsimony pressure in evolving the seeds for incremental genetic programming (IGP) are still poorly known and remain uninvestigated. In this work we present a study on the cumulative effect of the bloat and the seeding of the initial population on the efficiency of incremental evolution of simulated snake-like robot (Snakebot). In the proposed IGP, the task of coevolving the locomotion gaits and sensing of the bot in a challenging environment is decomposed into two sub-tasks, implemented as two consecutive evolutionary stages. First, to evolve the pools of sensorless Snakebots, we use GP featuring the following three bloat-control methods: (1) linear parametric parsimony pressure, (2) lexicographic parsimony pressure and (3) no bloat control. During the second stage of IGP, we use these pools to seed the initial population of Snakebots applying two methods of seeding: canonical seeding and seeding inspired by genetic transposition (GT).


Snakebot Genetic programming Bloat Genetic transposition Incremental GP 



The presented work is part of a project funded by Japan Society for the Promotion of Science (JSPS).


  1. 1.
    E. Alfaro-Cid, J.J. Merelo, de F.F. Vega, A.I. Esparcia-Alcazar, K. Sharman, Bloat control operators and diversity in genetic programming: A comparative study. Evol. Comput. 18, 2–305332 (2010)CrossRefGoogle Scholar
  2. 2.
    W. Beaudoin, S. Verel, P. Collard, C. Escazu. Deceptiveness and neutrality: The nd family of fitness landscapes. In GECCO 2006: Proceedings of the 2006 conference on genetic and evolutionary computation, pp. 507–514 (2006)Google Scholar
  3. 3.
    M. Brameier and W. Banzhaf. Neutral variations cause bloat in linear gp. In Proceedings of the 6th European conference on Genetic programming, EuroGP’03, (Springer, Verlag, 2003), pp. 286–296Google Scholar
  4. 4.
    M. Collins. Finding needles in haystacks is harder with neutrality. In GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, vol. 2, pp. 1613–1618, 2005.Google Scholar
  5. 5.
    B. Doerr, M. Gnewuch, N. Hebbinghaus, and F. Neumann. A rigorous view on neutrality. In Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, pp. 2591 –2597, sept. 2007.Google Scholar
  6. 6.
    M. Ebner. On the search space of genetic programming and its relation to nature’s search space. In Proceedings of the 1999 Congress on Evolutionary Computation. CEC 99, pp. 1357–1361, 1999.Google Scholar
  7. 7.
    E. Galván-López and R. Poli. An empirical investigation of how and why neutrality affects evolutionary search. In Proceedings of the 8th annual conference on Genetic and evolutionary computation, GECCO ’06, pp. 1149–1156, New York, 2006. ACM.Google Scholar
  8. 8.
    E. Galv’an-L’opez, R. Poli, A. Kattan, M. O’Neill, A. Brabazon, Neutrality in evolutionary algorithms… what do we know?. Evol. Syst. 2, 145–163 (2011)CrossRefGoogle Scholar
  9. 9.
    S. Gelly, O. Teytaud, N. Bredeche, M. Schoenauer, Universal Consistency and Bloat in GP. Revue d’Intelligence Artificielle, 20, 805–827 (2006)CrossRefGoogle Scholar
  10. 10.
    F. Gomez, R. Miikkulainen, Incremental evolution of complex general behavior. Adapt. Behav. 5, 5–317 (1997)CrossRefGoogle Scholar
  11. 11.
    M.A. Huynen, P.F. Stadler, W. Fontana (1996) Smoothness within ruggedness: the role of neutrality in adaptation. In Proceedings of the National Academy of Sciences of the United States of America, vol. 93, pp. 397–401Google Scholar
  12. 12.
    M. Kimura, The neutral theory of molecular evolution. (Cambridge University Press, Cambridge, 1983)CrossRefGoogle Scholar
  13. 13.
    K.E. Kinnear Jr. Fitness landscapes and difficulty in genetic programming. In Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence, Proceedings of the First IEEE Conference on, pp 142 –147 vol.1, jun (1994)Google Scholar
  14. 14.
    J.R. Koza, Genetic programming II: automatic discovery of reusable programs. (MIT Press, Cambridge, MA, USA, 1994)MATHGoogle Scholar
  15. 15.
    J.R. Koza, M.A. Keane, J. Yu, F.H. Bennett, W. Mydlowec, Automatic creation of human-competitive programs and controllers by means of genetic programming. Genet. Program. Evolvable Mach. 1, 121–164 (2000)CrossRefMATHGoogle Scholar
  16. 16.
    T. Kuyucu, I.Tanev, and K. Shimohara. Incremental genetic programming via genetic transpositions for efficient coevolution of locomotion and sensing of simulated snake-like robot. In European Conference on Artificial Life, pp. 439–446 (2011)Google Scholar
  17. 17.
    W.B. Langdon, P. Nordin. Seeding genetic programming populations. In Proceedings of the European Conference on Genetic Programming, (Springer, London, 2000), pp. 304–315Google Scholar
  18. 18.
    J. Lobo, J.H. Miller, W. Fontana. Neutrality in technological landscapes. Santa Fe Working Paper, 2004.Google Scholar
  19. 19.
    S. Luke, L. Panait. Lexicographic parsimony pressure. In GECCO 2002: Proceedings of the genetic and evolutionary computation conference, (Morgan Kaufmann Publishers, New York, 2002), pp. 829–836.Google Scholar
  20. 20.
    S. Luke, L. Panait, A comparison of bloat control methods for genetic programming. Evol. Comput. 14, 309–344 (2006)CrossRefGoogle Scholar
  21. 21.
    B. McClintock, The origin and behaviour of mutable loci in maize. Proc. Natl. Acad. Sci. U S A 36, 344–355 (1950)CrossRefGoogle Scholar
  22. 22.
    H.J. Morowitz, The Emergence of Everything: How the World Became Complex. (Oxford University Press, Oxford, 2002)Google Scholar
  23. 23.
    S. Nolfi, D. Floreano, O. Miglino, and F. Mondada. How to evolve autonomous robots: different approaches in evolutionary robotics. In 4th International Workshop on Artificial Life. MA: MIT Press (1994)Google Scholar
  24. 24.
    M. Nowacki, B.P. Higgins, G.M. Maquilan, E.C. Swart, ThomasG. Doak, F. Laura, Landweber. A functional role for transposases in a large eukaryotic genome. Science 324(5929), 935–938 (2009)Google Scholar
  25. 25.
    J.E. Perry. The effect of population enrichment in genetic programming. In Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on, pages 456 –461 vol.1, June (1994)Google Scholar
  26. 26.
    R. Poli and N.F. McPhee. Covariant parsimony pressure for genetic programming. Technical Report CES-480, Department of Computing and Electronic Systems, University of Essex, UK, (2008)Google Scholar
  27. 27.
    R. Shipman. Genetic redundancy: Desirable or problematic for evolutionary adaptation. In 4th International Conf. on Artificial Neural Networks and Genetic Algorithms (ICANNGA’99), pp.1–11, (1999)Google Scholar
  28. 28.
    R. Smith. Open Dynamics Engine., (2004)
  29. 29.
    D.J. Strand, J.F. McDonald, Copia is transcriptionally responsive to environmental stress. Nucleic Acids Res. 13(12), 4401–4410 (1985)CrossRefGoogle Scholar
  30. 30.
    I. Tanev, T. Ray, A. Buller, Automated evolutionary design, robustness and adaptation of sidewinding locomotion of simulated snake-like robot. IEEE Trans. Rob. 21, 632–645 (2005)CrossRefGoogle Scholar
  31. 31.
    I. Tanev and K. Shimohara. Co-evolution of active sensing and locomotion gaits of simulated snake-like robot. In Proceedings of the 10th annual conference on Genetic and evolutionary computation, GECCO ’08, pp. 257–264, New York, 2008. ACMGoogle Scholar
  32. 32.
    I. Tanev, Dom/xml-based portable genetic representation of the morphology, behavior and communication abilities of evolvable agents. Artif. Life Rob. 8, 52–56 (2004)CrossRefGoogle Scholar
  33. 33.
    R. Thomsen, G.B. Fogel, and T. Krink. A clustal alignment improver using evolutionary algorithms. In Evolutionary Computation, 2002. CEC ’02. Proceedings of the 2002 Congress on, volume 1, pp. 121–126, May (2002)Google Scholar
  34. 34.
    V.K. Vassilev, Dominic Job, and Julian F. Miller. Towards the automatic design of more efficient digital circuits. In EH ’00: Proceedings of the 2nd NASA/DoD workshop on Evolvable Hardware, page 151, Washington, DC, USA, 2000. IEEE Computer SocietyGoogle Scholar
  35. 35.
    V.K. Vassilev and J.F. Miller. The advantages of landscape neutrality in digital circuit evolution. In Proceedings of the 3rd International Conference on Evolvable Systems: From Biology to Hardware, pp. 252–26. Springer, Berlin, 2000.Google Scholar
  36. 36.
    A. Wagner, Robustness, evolvability, and neutrality. FEBS Lett. 579(8), 1772–1778 (2005)CrossRefGoogle Scholar
  37. 37.
    C.O. Wilke, J.L. Wang, C. Ofria, R.E. Lenski, C. Adami, Evolution of digital organisms at high mutation rates leads to survival of the flattest. Nature 412, 331–333 (2001)CrossRefGoogle Scholar
  38. 38.
    T. Yu and J.F. Miller. The role of neutral and adaptive mutation in an evolutionary search on the onemax problem. In GECCO Late Breaking Papers’02, pp. 512–519, (2002)Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Doshisha UniversityKyotanabeJapan

Personalised recommendations