Symbolic Regression with Sampling

Michael Kommenda, Gabriel Kronberger, Michael Affenzeller, Stephan Winkler, Christoph Feilmayr, Stefan Wagner

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

3 Citations (Scopus)


In this paper a way of improving the performance of genetic programming (GP) for regression tasks is presented. In general, most of the execution time is consumed during the evaluation step of an individual. Hence reducing the number of samples which are evaluated during the learning phase of the algorithm significantly reduces its execution time. A reduction of the available training samples might hamper the algorithm in its capability to learn the desired correlation. For this reason our approach evaluates each solution only on a randomly chosen part of all training samples, which is selected before the evaluation step. In the result section runs with different parameter settings of our approach and traditional genetic programming algorithms are compared regarding the solution quality and execution time to each other.
Original languageEnglish
Title of host publication22th European Modeling and Simulation Symposium, EMSS 2010
Number of pages6
Publication statusPublished - 2010
Event22nd European Modeling and Simulation Symposium EMSS 2010 - Fes, Morocco
Duration: 13 Oct 201015 Oct 2010

Publication series

Name22th European Modeling and Simulation Symposium, EMSS 2010


Conference22nd European Modeling and Simulation Symposium EMSS 2010
Internet address


  • Genetic Programming
  • Symoblic Regression
  • Sampling
  • Machine Learning
  • Performance Analysis
  • Symbolic regression
  • Genetic programming
  • Machine learning
  • Performance analysis


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