DGP: How To Improve Genetic Programming with Duals

  • J.-L. Segapeli
  • C. Escazut
  • P. Collard


In this paper, we present a new approach, improving the performances of a genetic algorithm (GA). Such algorithms are iterative search procedures based on natural genetics. We use an original genetic algorithm that manipulates pairs of twins in its population: DGA, dual-based genetic algorithm. We show that this approach is relevant for genetic programming (GP), which manipulates populations of trees. In particular, we show that duals can transform a deceptive problem into a convergent one. We also prove that using pairs of dual functions in the primitive function set, is more efficient in the problem of learning boolean functions. Here, in order to prove the theoretical interest of our approach (DGP: dual-based genetic programming), we perform a numerical simulation.


Genetic Algorithm Genetic Programming Boolean Function Dual Function Internal Function 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    P. Collard and J.-P. Aurand. DGA: An Efficient Genetic Algorithm. ECAI’94. 1994.Google Scholar
  2. [2]
    P. Collard and J.-L. Segapeli. Using a Double-based Genetic Algorithm on a Population of Computer Programs. In ICTAI’94: Proceedings of the 6th IEEE International Conference on Tools with Artificial Intelligence. New Orleans. USA. 1994.Google Scholar
  3. [3]
    D.E. Goldberg. Simple Genetic Algorithms and the Minimal Deceptive Problem. Genetic Algorithms and Simulated Annealing. L. Davis ed. 1987.Google Scholar
  4. [4]
    J.R. Koza. Genetic Programming. MIT Press, Cambridge, MA. 1992.MATHGoogle Scholar
  5. [5]
    D.J. Montana. Strongly Typed Genetic Programming. BBN Technical Report #7866. 1993.Google Scholar
  6. [6]
    M.D. Vose. Generalizing the notion of schema in genetic algorithms. Artificial Intelligence, 50: 385–396. 1991.MathSciNetMATHCrossRefGoogle Scholar
  7. [7]
    D. Whitley. An Executable Model of a Simple Genetic Algorithm. Foundations of Genetic Algorithms 2, Morgan Kaufmann. 1993.Google Scholar

Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • J.-L. Segapeli
    • 1
  • C. Escazut
    • 1
  • P. Collard
    • 1
  1. 1.Laboratory I3S-CNRS UNSAValbonneFrance

Personalised recommendations