Experiments on Controlling Overfitting in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

@InProceedings{goncalves2011experiments,
  author =       "Ivo Goncalves and Sara Silva",
  title =        "Experiments on Controlling Overfitting in Genetic
                 Programming",
  booktitle =    "Local proceedings of the 15th Portuguese Conference on
                 Artificial Intelligence: Progress in Artificial
                 Intelligence",
  year =         "2011",
  series =       "EPIA 2011",
  pages =        "152--166",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, overfitting,
                 generalization",
  isbn13 =       "978-989-95618-4-7",
  URL =          "https://www.cisuc.uc.pt/publication/show/2653",
  URL =          "https://www.cisuc.uc.pt/publication/showfile?fn=1512948575_Experiments_on_Controlling_Overfitting_in_Genetic_Programming.pdf",
  size =         "15 pages",
  abstract =     "One of the most important goals of any Machine
                 Learning approach is to find solutions that perform
                 well not only on the cases used for learning but also
                 on cases never seen before. This is known as
                 generalization ability, and failure to do so is called
                 over-fitting. In Genetic Programming this issue has not
                 yet been given the attention it deserves, although the
                 number of publications on this subject has been
                 increasing in the past few years. Here we perform
                 several experiments on a small and yet difficult toy
                 problem specifically designed for this work, where a
                 perfect fitting of the training data inevitably results
                 in poor generalization on the unseen test data. The
                 results show that, on this problem, a Random Sampling
                 Technique with parameter settings that maximize the
                 variation between generations can significantly reduce
                 over fitting when compared to a standard GP approach.
                 We also report the results of some techniques that
                 failed to achieve better generalization.",
  notes =        "Not in EPIA-2011 LNCS 7026 published by Springer",
}

Genetic Programming entries for Ivo Goncalves Sara Silva

Citations