Model Selection and Overfitting in Genetic Programming: Empirical Study [Extended Version]
Abstract
Genetic Programming has been very successful in solving a large area of problems but its use as a machine learning algorithm has been limited so far. One of the reasons is the problem of overfitting which cannot be solved or suppresed as easily as in more traditional approaches. Another problem, closely related to overfitting, is the selection of the final model from the population. In this article we present our research that addresses both problems: overfitting and model selection. We compare several ways of dealing with ovefitting, based on Random Sampling Technique (RST) and on using a validation set, all with an emphasis on model selection. We subject each approach to a thorough testing on artificial and real--world datasets and compare them with the standard approach, which uses the full training data, as a baseline.
Cite
@article{arxiv.1504.08168,
title = {Model Selection and Overfitting in Genetic Programming: Empirical Study [Extended Version]},
author = {Jan Žegklitz and Petr Pošík},
journal= {arXiv preprint arXiv:1504.08168},
year = {2015}
}
Comments
8 pages, 12 figures, full paper for GECCO 2015 (accepted as poster, this is the original paper submitted to the conference); added subtitle and removed copyright text at the first page, fixed some typography