Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A comparison of bloat control methods for genetic programming.

Sean Luke1, Liviu Panait

  • 1Department of Computer Science, George Mason University, Fairfax, VA 22030, USA. sean@cs.gmu.edu

Evolutionary Computation
|August 15, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Subnormothermic Oxygenated Perfusion Optimally Preserves Donor Kidneys <i>Ex Vivo</i>.

Kidney international reports·2019
Same author

Randomized open-label crossover assessment of Prograf vs Advagraf on immunosuppressant pharmacokinetics and pharmacodynamics in simultaneous pancreas-kidney patients.

Clinical transplantation·2017
Same author

Theoretical convergence guarantees for cooperative coevolutionary algorithms.

Evolutionary computation·2010
Same author

Modification point depth and genome growth in genetic programming.

Evolutionary computation·2003
Same journal

Computing Optimal Populations for Binary Problems using Logic Minimization.

Evolutionary computation·2026
Same journal

Enhancing Generalization and Scalability for Multi-Objective Optimization with Population Pre-Training.

Evolutionary computation·2026
Same journal

XCS for Sequential Perceptual Aliasing in Multi-Step Decision Making.

Evolutionary computation·2026
Same journal

A dynamic multi-objective evolutionary algorithm using dual-space prediction and surrogate-based sampling.

Evolutionary computation·2026
Same journal

Adapting MOEA/D to CMA-ES for Dealing with Ill-conditioned Multiobjective Problems.

Evolutionary computation·2026
Same journal

Editorial of the Special Issue: Parallel Problem Solving from Nature PPSN 2024 Extended Versions of Best Paper Candidates.

Evolutionary computation·2026
See all related articles

This study compares nine bloat control methods in genetic programming, finding that combining depth limiting with punitive measures is most effective at reducing bloat. An unexpected cross-platform winner emerged.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Evolutionary Computation

Background:

  • Bloat, or uncontrolled size growth, is a significant problem in genetic programming (GP).
  • Depth limiting is a common but not always sufficient method to control bloat in tree-based GP individuals.
  • Combining depth limiting with size-based penalties shows promise for more effective bloat control.

Purpose of the Study:

  • To investigate and compare the effectiveness of various bloat control methods in genetic programming.
  • To determine which combinations of depth limiting and other bloat control techniques are most successful.
  • To identify problem-specific and generalizable settings for optimal bloat reduction.

Main Methods:

  • Augmenting depth limiting with nine distinct bloat control methods from literature and novel approaches.

Related Experiment Videos

  • Conducting experiments across four diverse genetic programming problems.
  • Analyzing the performance of each method on a per-problem basis and across problems.

    • Main Results:

    • Depth limiting combined with punitive methods generally outperforms either technique alone.
    • Specific bloat control methods show varying effectiveness depending on the genetic programming problem.
    • An unexpected bloat control method demonstrated superior performance in a cross-platform evaluation.

    Conclusions:

    • Combining depth limiting with carefully selected punitive bloat control strategies is highly effective in genetic programming.
    • The optimal bloat control strategy can be problem-dependent, necessitating tailored approaches.
    • Further research into cross-platform bloat control methods is warranted, with one method showing particular promise.