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Competent Geometric Semantic Genetic Programming for Symbolic Regression and Boolean Function Synthesis.

Tomasz P Pawlak1, Krzysztof Krawiec2

  • 1Institute of Computing Science, Poznan University of Technology, Poznań, Poland tpawlak@cs.put.poznan.pl.

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Summary
This summary is machine-generated.

This study introduces competent operators for Genetic Programming (GP) that enhance program effectiveness and geometric properties. These novel operators outperform existing methods across various performance metrics.

Keywords:
Semanticseffectivenessexperiment.geometrymetricstheory

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Evolutionary Computation

Background:

  • Program semantics is an emerging research area in Genetic Programming (GP).
  • Existing semantic-aware operators in GP focus on either geometric properties or offspring effectiveness, with few addressing both.
  • A gap exists in GP operator design for simultaneously optimizing semantic space geometry and offspring effectiveness.

Purpose of the Study:

  • To propose a novel suite of competent operators for Genetic Programming (GP).
  • These operators aim to synergistically combine semantic effectiveness and geometric properties.
  • To evaluate the performance of these operators in population initialization, mate selection, mutation, and crossover.

Main Methods:

  • Development of a theoretical framework for competent GP operators.
  • Implementation of operators integrating effectiveness and geometric properties for GP.
  • Experimental comparison against established GP operators on symbolic regression and Boolean function synthesis benchmarks.

Main Results:

  • The proposed competent operators demonstrate superior performance across multiple indicators.
  • Analysis shows effectiveness when operators are used individually and synergistically within an evolutionary run.
  • Improvements observed in best-of-run fitness, test-set fitness, and program size compared to existing methods.

Conclusions:

  • The developed competent operators offer a significant advancement in Genetic Programming.
  • These operators effectively balance semantic distinctiveness and exploration of the semantic space.
  • The findings suggest competent operators are a superior choice for GP tasks like symbolic regression and Boolean function synthesis.