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Santa Fe Trail Hazards

  • Denis Robilliard
  • Sébastien Mahler
  • Dominique Verhaghe
  • Cyril Fonlupt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3871)

Abstract

This paper focuses on methodological problems associated to the famous Santa Fe Trail (SFT) problem, a very common benchmark for evaluating Genetic Programming (GP) algorithms, introduced by Koza in its first book on GP. We put in evidence the difficulty to ensure fair comparisons especially with new genotype representations as found in works on grammar-based automatic programming, such as Grammatical Evolution, and Bayesian Automatic Programming. We extend a work by Langdon et al. by measuring the effort to solve SFT by random search with different time steps limits and a reduced but semantically equivalent function set.

Keywords

Random Search Automatic Programming Derivation Rule Conditional Probability Table Grammatical Evolution 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Denis Robilliard
    • 1
  • Sébastien Mahler
    • 1
  • Dominique Verhaghe
    • 1
  • Cyril Fonlupt
    • 1
  1. 1.Laboratoire d’Informatique du LittoralUniversité du Littoral-Côte d’OpaleCalaisFrance

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