Undirected Training of Run Transferable Libraries

  • Maarten Keijzer
  • Conor Ryan
  • Gearoid Murphy
  • Mike Cattolico
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3447)


This paper investigates the robustness of Run Transferable Libraries(RTLs) on scaled problems. RTLs provide GP with a library of functions which replace the usual primitive functions provided when approaching a problem. The RTL evolves from run to run using feedback based on function usage, and has been shown to outperform GP by an order of magnitude on a variety of scalable problems.

RTLs can, however, also be applied across a domain of related problems, as well as across a range of scaled instances of a single problem. To do this successfully, it will need to balance a range of functions. We introduce a problem that can deceive the system into converging to a sub-optimal set of functions, and demonstrate that this is a consequence of the greediness of the library update algorithm.

We demonstrate that a much simpler, truly evolutionary, update strategy doesn’t suffer from this problem, and exhibits far better optimization properties than the original strategy.


Genetic Program Problem Instance Genetic Program System Multiplexer Problem Library Content 
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.
    Angeline, P.J., Pollack, J.B.: The evolutionary induction of subroutines. In: Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society, Bloomington, Indiana, USA. Lawrence Erlbaum, Mahwah (1992)Google Scholar
  2. 2.
    Keijzer, M., Ryan, C., Cattolico, M.: Run transferable libraries — learning functional bias in problem domains. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, 13-17 July. Springer, Heidelberg (2004)Google Scholar
  3. 3.
    Koza, J.R.: Genetic programming: A paradigm for genetically breeding populations of computer programs to solve problems. Technical Report STAN-CS-90-1314, Computer Science Department, Stanford University (1990)Google Scholar
  4. 4.
    Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)zbMATHGoogle Scholar
  5. 5.
    Roberts, S.C., Howard, D., Koza, J.R.: Evolving modules in genetic programming by subtree encapsulation. In: Miller, J.F., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 160–175. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  6. 6.
    Rosca, J.P., Ballard, D.H.: Hierarchical self-organization in genetic programming. In: Proceedings of the Eleventh International Conference on Machine Learning. Morgan Kaufmann, San Francisco (1994)Google Scholar
  7. 7.
    Ryan, C., Keijzer, M., Cattolico, M.: Favourable biasing of function sets. In: Riolo, R., O’Reilly, U.-M., Yu, T. (eds.) Proceedings of the second Genetic Programming Theory and Practice Workshop. MIT Press, Cambridge (2004) (to appear)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Maarten Keijzer
    • 1
  • Conor Ryan
    • 2
  • Gearoid Murphy
    • 2
  • Mike Cattolico
    • 3
  1. 1.PrognosysNetherlands
  2. 2.University of LimerickIreland
  3. 3.Tiger Mountain ScientificSeattleUSA

Personalised recommendations