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Informed Down-Sampled Lexicase Selection: Identifying Productive Training Cases for Efficient Problem Solving.

Ryan Boldi1, Martin Briesch2, Dominik Sobania3

  • 1University of Massachusetts, Amherst, MA 01003, USA rbahlousbold@umass.edu.

Evolutionary Computation
|January 25, 2024
PubMed
Summary
This summary is machine-generated.

Informed Down-Sampled Lexicase Selection improves Genetic Programming (GP) by using population statistics to select more informative training cases. This method significantly outperforms random sampling in program synthesis benchmarks.

Keywords:
Genetic programminginformed down-samplinglexicase selection

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Area of Science:

  • Artificial Intelligence
  • Evolutionary Computation

Background:

  • Genetic Programming (GP) typically requires evaluating all individuals on extensive training datasets.
  • Random down-sampled lexicase selection offers efficiency but risks excluding crucial training cases or overusing synonymous ones.

Purpose of the Study:

  • To introduce and evaluate Informed Down-Sampled Lexicase Selection for Genetic Programming.
  • To enhance training case selection by leveraging population statistics for more informative down-samples.

Main Methods:

  • Developed Informed Down-Sampled Lexicase Selection using population statistics to identify distinct training cases.
  • Empirically investigated the method across PushGP and Grammar-Guided GP systems on program synthesis benchmarks.

Main Results:

  • Informed down-sampling significantly outperformed random down-sampling in program synthesis tasks.
  • Ensured consistent inclusion of important training cases across evolutionary runs and systems.

Conclusions:

  • Informed Down-Sampled Lexicase Selection provides superior performance over random methods in GP.
  • The approach likely maintains specialist individuals while reducing evaluation costs, leading to improved evolutionary outcomes.