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Dynamic Grammar Pruning for Program Size Reduction in Symbolic Regression.

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Summary
This summary is machine-generated.

This study introduces a Production Ranking method to automatically reduce bloated grammars in grammar-based genetic programming. The approach successfully reduced program size and improved generalization performance in symbolic regression tasks.

Keywords:
Effective genome lengthGrammar pruningGrammatical evolutionProduction ranking

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Evolutionary Computation

Background:

  • Grammar design is crucial in grammar-based genetic programming, impacting performance and program size.
  • Current grammar design often requires expert knowledge, lacking automated approaches.
  • Bloated grammars can hinder evolutionary search efficiency and solution quality.

Purpose of the Study:

  • To develop an automatic approach for reducing bloated grammars in grammar-based genetic programming.
  • To investigate the impact of dynamic grammar pruning on program size and generalization performance.
  • To enhance evolutionary search by channeling it towards smaller, effective solutions.

Main Methods:

  • A Production Ranking mechanism was employed to identify and dynamically prune less useful grammar productions.
  • The approach was tested on 13 symbolic regression datasets using Grammatical Evolution.
  • Comparisons were made against a baseline grammar, evaluating effective genome length and test performance.
  • Linear scaling was integrated during production ranking stages for performance enhancement.

Main Results:

  • Dynamic grammar pruning significantly reduced genome lengths across all datasets.
  • Initial pruning improved generalization on three datasets but worsened on five.
  • Integrating linear scaling dramatically improved results, yielding smaller programs and better generalization on 6 out of 13 datasets.
  • Even when the baseline was linearly scaled, the Production Ranking approach maintained smaller program sizes with comparable generalization.

Conclusions:

  • Automatic grammar reduction using Production Ranking is effective for decreasing program size in Grammatical Evolution.
  • The integration of linear scaling significantly enhances both program size reduction and generalization performance.
  • This automated approach offers a promising alternative to manual grammar design, improving evolutionary search efficiency.