Abstract

A new Genetic Programming variant called Liquid State Genetic Programming (LSGP) is proposed in this paper. LSGP is a hybrid method combining a dynamic memory for storing the inputs (the liquid) and a Genetic Programming technique used for the problem solving part. Several numerical experiments with LSGP are performed by using several benchmarking problems. Numerical experiments show that LSGP performs similarly and sometimes even better than standard Genetic Programming for the considered test problems.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  2. 2.
    Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Subprograms. MIT Press, Cambridge (1994)Google Scholar
  3. 3.
    Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14, 2531–2560 (2002)MATHCrossRefGoogle Scholar
  4. 4.
    Miller, J.F., Job, D., Vassilev, V.K.: Principles in the Evolutionary Design of Digital Circuits - Part I. Genetic Programming and Evolvable Machines 1(1-2), 7–35 (2000)MATHCrossRefGoogle Scholar
  5. 5.
    Natschläger, T., Maass, W., Markram, H.: The ”liquid computer”: A novel strategy for real-time computing on time series. Special Issue on Foundations of Information Processing of TELEMATIK 8, 39–43 (2002)Google Scholar
  6. 6.
    Poli, R., Langdon, W.B.: Sub-machine Code Genetic Programming. In: Spector, L., Langdon, W.B., O’Reilly, U.-M., Angeline, P.J. (eds.) Advances in Genetic Programming 3, MIT Press, Cambridge (1999)Google Scholar
  7. 7.
    Poli, R., Page, J.: Solving High-Order Boolean Parity Problems with Smooth Uniform Crossover, Sub-Machine Code GP and Demes. Journal of Genetic Programming and Evolvable Machines, Kluwer, 1–21 (2000)Google Scholar
  8. 8.
    Prechelt, L.: PROBEN1: A Set of Neural Network Problems and Benchmarking Rules, Technical Report 21, University of Karlsruhe (1994), available from ftp://ftp.cs.cmu.edu/afs/cs/project/connect/bench/contrib/prechelt/proben1.tar.gz
  9. 9.
    UCI Machine Learning Repository, available from http://www.ics.uci.edu/~mlearn/MLRepository.html

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Mihai Oltean
    • 1
  1. 1.Department of Computer Science, Faculty of Mathematics and Computer Science, Babeş-Bolyai University, Kogălniceanu 1, Cluj-Napoca, 3400Romania

Personalised recommendations