Improving the parsimony of regression models for an enhanced genetic programming process

Alexandru-Ciprian Zavoianu, Gabriel Kronberger, Michael Kommenda, Daniela Zaharie, Michael Affenzeller

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

2 Citations (Scopus)

Abstract

This research is focused on reducing the average size of the solutions generated by an enhanced GP process without affecting the high predictive accuracy the method exhibits when being applied on a complex, industry proposed, regression problem. As such, the effects the GP enhancements have on bloat have been studied and, finally, a bloat control system based on dynamic depth limiting (DDL) and iterated tournament pruning (ITP) was designed. The resulting bloat control system is able to improve by ≃ 40% the average GP solution parsimony without impacting average solution accuracy.

Original languageEnglish
Title of host publicationComputer Aided Systems Theory, EUROCAST 2011 - 13th International Conference, Revised Selected Papers
Pages264-271
Number of pages8
Volume6927
EditionPART 1
DOIs
Publication statusPublished - 2012
Event13th International Conference on Computer Aided Systems Theory, Eurocast 2011 - Las Palmas de Gran Canaria, Spain
Duration: 6 Feb 201111 Feb 2011
http://www.iuctc.ulpgc.es/spain/eurocast2011/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6927 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Computer Aided Systems Theory, Eurocast 2011
CountrySpain
CityLas Palmas de Gran Canaria
Period06.02.201111.02.2011
Internet address

Keywords

  • bloat control
  • genetic programming
  • solution parsimony
  • symbolic regression

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