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On Improving Generalisation in Genetic Programming

  • Dan Costelloe
  • Conor Ryan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5481)

Abstract

This paper is concerned with the generalisation performance of GP. We examine the generalisation of GP on some well-studied test problems and also critically examine the performance of some well known GP improvements from a generalisation perspective. From this, the need for GP practitioners to provide more accurate reports on the generalisation performance of their systems on problems studied is highlighted. Based on the results achieved, it is shown that improvements in training performance thanks to GP-enhancements represent only half of the battle.

Keywords

Test Problem Genetic Program Generalisation Performance Linear Scaling Unseen Data 
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 2009

Authors and Affiliations

  • Dan Costelloe
    • 1
  • Conor Ryan
    • 1
  1. 1.BDS Group, Department of Computer Science and Information SystemsUniversity of LimerickLimerickIreland

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