Experiments on Controlling Overfitting in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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://old.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