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Related Experiment Video

Updated: Feb 9, 2026

A Quantitative Fitness Analysis Workflow
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Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming.

Michaela Drahosova1, Lukas Sekanina2, Michal Wiglasz3

  • 1Brno University of Technology, Faculty of Information Technology, IT4Innovations Centre of Excellence, Bozetechova 2, 612 66 Brno, Czech Republic idrahosova@fit.vutbr.cz.

Evolutionary Computation
|June 5, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method to optimize fitness predictor size in genetic programming (GP). This significantly reduces the time needed for evolutionary algorithm tuning and program evolution.

Keywords:
Cartesian genetic programmingcoevolutionary algorithmsevolutionary designfitness predictionimage processing.symbolic regression

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Evolutionary Computation

Background:

  • Genetic programming (GP) often coevolves programs with fitness predictors, which are subsets of training data.
  • Optimizing the size of these fitness predictors is crucial for GP performance but is a time-consuming, application-dependent process.

Purpose of the Study:

  • To propose a novel method for automatically adapting fitness predictors and their sizes within GP.
  • To reduce the computational time required for both evolutionary algorithm tuning and program evolution in GP.

Main Methods:

  • Developed and implemented an automatic predictor adaptation method.
  • Integrated the method within the Cartesian genetic programming (CGP) framework.
  • Evaluated the approach on symbolic regression and image filter design problems.

Main Results:

  • The proposed method automatically determined optimal predictor sizes for specific problems.
  • Significant reductions in CGP search time were observed compared to three different CGP implementations.
  • The quality of the evolved solutions remained comparable to existing methods.

Conclusions:

  • Automatic adaptation of fitness predictors offers a more efficient approach to GP.
  • This method effectively minimizes the time investment required for parameter tuning and evolutionary search.
  • The approach is broadly applicable to various problems within GP, including symbolic regression and image processing.