On the Evolutionary Behavior of Genetic Programming with Constants Optimization

  • Bogdan Burlacu
  • Michael Affenzeller
  • Michael Kommenda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8111)


Evolutionary systems are characterized by two seemingly contradictory properties: robustness and evolvability. Robustness is generally defined as an organism’s ability to withstand genetic perturbation while maintaining its phenotype. Evolvability, as an organism’s ability to produce useful variation. In genetic programming, the relationship between the two, mediated by selection and variation-producing operators (recombination and mutation), makes it difficult to understand the behavior and evolutionary dynamics of the search process. In this paper, we show that a local gradient-based constants optimization step can improve the overall population evolvability by inducing a beneficial structure-preserving bias on selection, which in the long term helps the process maintain diversity and produce better solutions.


Genetic Programming Evolutionary Behavior Constant Optimization Symbolic Regression Algorithm Analysis 


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  1. 1.
    Iba, H., Sato, T., de Garis, H.: Recombination guidance for numerical genetic programming. In: 1995 IEEE Conference on Evolutionary Computation, November 29-December 1, vol. 1, pp. 97–102. IEEE Press, Perth (1995)CrossRefGoogle Scholar
  2. 2.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  3. 3.
    Kommenda, M., Affenzeller, M., Kronberger, G., Winkler, S.: Nonlinear Least Squares Optimization of Constants in Symbolic Regression (2013)Google Scholar
  4. 4.
    Majeed, H., Ryan, C.: Using context-aware crossover to improve the performance of gp. In: GECCO 2006: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 847–854. ACM Press (2006)Google Scholar
  5. 5.
    McKay, B., Willis, M., Barton, G.: Using a tree structured genetic algorithm to perform symbolic regression. In: First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA (Conf. Publ. No. 414), pp. 487–492 (September 1995)Google Scholar
  6. 6.
    Rutherford, S.L.: From genotype to phenotype: buffering mechanisms and the storage of genetic information. BioEssays 22(12), 1095–1105 (2000)CrossRefGoogle Scholar
  7. 7.
    Topchy, A., Punch, W.F.: Faster genetic programming based on local gradient search of numeric leaf values. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 155–162. Morgan Kaufmann (2001)Google Scholar
  8. 8.
    de Visser, J.A.G.M., Hermisson, J., Wagner, G.P., Meyers, L.A., Bagheri-Chaichian, H., Blanchard, J.L., Chao, L., Cheverud, J.M., Elena, S.F., Fontana, W., Gibson, G., Hansen, T.F., Krakauer, D., Lewontin, R.C., Ofria, C., Rice, S.H., von Dassow, G., Wagner, A., Whitlock, M.C.: Perspective: Evolution and detection of genetic robustness. Evolution 57(9), 1959–1972 (2003)Google Scholar
  9. 9.
    Wagner, S.: Heuristic Optimization Software Systems - Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment. Ph.D. thesis, Institute for Formal Models and Verification, Johannes Kepler University, Linz, Austria (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bogdan Burlacu
    • 1
  • Michael Affenzeller
    • 1
  • Michael Kommenda
    • 1
  1. 1.Heuristic and Evolutionary Algorithms LaboratoryUniversity of Applied Sciences Upper AustriaHagenbergAustria

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