Overfitting detection and adaptive covariant parsimony pressure for symbolic regression

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

4 Citations (Scopus)

Abstract

Covariant parsimony pressure is a theoretically motivated method primarily aimed to control bloat. In this contribution we describe an adaptive method to control covariant parsimony pressure that is aimed to reduce overfitting in symbolic regression. The method is based on the assumption that overfitting can be reduced by controlling the evolution of program length. Additionally, we propose an overfitting detection criterion that is based on the correlation of the fitness values on the training set and a validation set of all models in the population. The proposed method uses covariant parsimony pressure to decrease the average program length when overfitting occurs and allows an increase of the average program length in the absence of overfitting. The proposed approach is applied on two real world datasets. The experimental results show that the correlation of training and validation fitness can be used as an indicator for overfitting and that the proposed method of covariant parsimony pressure adaption alleviates overfitting in symbolic regression experiments with the two datasets.

Original languageEnglish
Title of host publicationGenetic and Evolutionary Computation Conference, GECCO'11 - Companion Publication
PublisherACM Sigevo
Pages631-638
Number of pages8
ISBN (Print)9781450306904
DOIs
Publication statusPublished - 2011
Event13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 - Dublin, Ireland
Duration: 12 Jul 201116 Jul 2011

Publication series

NameGenetic and Evolutionary Computation Conference, GECCO'11 - Companion Publication

Conference

Conference13th Annual Genetic and Evolutionary Computation Conference, GECCO'11
CountryIreland
CityDublin
Period12.07.201116.07.2011

Keywords

  • overfitting
  • parsimony pressure
  • symbolic regression

Fingerprint Dive into the research topics of 'Overfitting detection and adaptive covariant parsimony pressure for symbolic regression'. Together they form a unique fingerprint.

Cite this