Statistical Genetic Programming: The Role of Diversity

  • Maryam Amir HaeriEmail author
  • Mohammad Mehdi Ebadzadeh
  • Gianluigi Folino
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 223)


In this chapter, a new GP-based algorithm is proposed. The algorithm, named SGP (Statistical GP), exploits statistical information, i.e. mean, variance and correlation-based operators, in order to improve the GP performance. SGP incorporates new genetic operators, i.e. Correlation Based Mutation, Correlation Based Crossover, and Variance Based Editing, to drive the search process towards fitter and shorter solutions. Furthermore, this work investigates the correlation between diversity and fitness in SGP, both in terms of phenotypic and genotypic diversity. First experiments conducted on four symbolic regression problems illustrate the goodness of the approach and permits to verify the different behavior of SGP in comparison with standard GP from the point of view of the diversity and its correlation with the fitness.


Genotypic Diversity Edit Distance Genetic Programming Model Tree Edit Distance High Phenotypic Diversity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Burke, E., Gustafson, S., Kendall, G.: Diversity in genetic programming: an analysis of measures and correlation with fitness. Trans. Evol. Comp. 8(1), 47–62 (2004)CrossRefGoogle Scholar
  2. 2.
    de Jong, E., Watson, R., Pollack, J.: Reducing bloat and promoting diversity using multi-objective methods (2001)Google Scholar
  3. 3.
    Ekárt, A., Németh, S.: A metric for genetic programs and fitness sharing. In: Genetic Programming, pp. 259–270. Springer, Berlin (2000)Google Scholar
  4. 4.
    Ekárt, A., Nemeth, S.: Maintaining the diversity of genetic programs. In: Genetic Programming, pp. 122–135 (2002)Google Scholar
  5. 5.
    Folino, G., Pizzuti, C., Spezzano, G., Vanneschi, L., Tomassini, M.: Diversity analysis in cellular and multipopulation genetic programming. In: CEC’03, vol. 1, pp. 305–311. IEEE (2003)Google Scholar
  6. 6.
    Jackson, D.: Mutation as a diversity enhancing mechanism in genetic programming. In: Proceedings of GECCO ’11, pp. 1371–1378. ACM, New York, (2011)Google Scholar
  7. 7.
    Langdon, W.: Genetic programming and data structures: genetic programming+ data structures. Springer, New York (1998)Google Scholar
  8. 8.
    Pennachin, C.L., Looks, M., de Vasconcelos, J.a.A.: Robust symbolic regression with affine arithmetic. In: Proceedings of GECCO ’10, pp. 917–924. ACM, New York (2010)Google Scholar
  9. 9.
    Rosca, J.: Entropy-driven adaptive representation. In: Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, vol. 9, pp. 23–32. Tahoe City (1995a)Google Scholar
  10. 10.
    Rosca, J.: Genetic programming: exploratory power and the discovery of functions. In: Proceedings of Evolutionary Programming IV, pp. 719–736. Citeseer (1995b)Google Scholar
  11. 11.
    Vladislavleva, E.J., Smits, G.F., Den Hertog, D.: Order of nonlinearity as a complexity measure for models generated by symbolic regression via pareto genetic programming. Trans. Evol. Comp. 13, 333–349 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Maryam Amir Haeri
    • 1
    Email author
  • Mohammad Mehdi Ebadzadeh
    • 1
  • Gianluigi Folino
    • 2
  1. 1.Department of Computer Engineering and Information TechnologyAmirkabir University of TechnologyTehranIran
  2. 2.ICAR-CNRRendeItaly

Personalised recommendations