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Zobrist hash-based duplicate detection in symbolic regression.

Bogdan Burlacu1

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Summary

Symbolic regression using genetic programming (GP) can be inefficient due to repeated calculations. A new caching method using Zobrist hashing speeds up GP by up to 34% without sacrificing accuracy.

Keywords:
Zobrist hashfitness cachegenetic diversitygenetic programmingsymbolic regressiontree hash

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Area of Science:

  • Computational Science
  • Machine Learning
  • Physical Sciences

Background:

  • Symbolic regression aims to find optimal functions for data without predefined models.
  • Genetic programming (GP) is a dominant evolutionary search technique for symbolic regression.
  • Standard GP implementations suffer from redundant computations, wasting computational resources.

Purpose of the Study:

  • To analyze the efficiency of evolutionary search in genetic programming.
  • To introduce a novel caching mechanism to mitigate redundant computations in GP.
  • To improve the runtime performance of symbolic regression without compromising search quality.

Main Methods:

  • Introduced a caching mechanism based on Zobrist hashing.
  • Implemented the caching approach within the open-source Operon framework.
  • Evaluated performance on various real-world regression problems.

Main Results:

  • Observed significant speedups, up to 34%, in computational time.
  • Demonstrated that the caching mechanism does not negatively impact search quality.
  • The Zobrist hashing approach efficiently avoids re-evaluation of previously computed states.

Conclusions:

  • Zobrist hashing provides a straightforward method to enhance GP runtime performance.
  • This caching strategy offers potential for adaptive search adjustments based on historical data.
  • The technique is particularly relevant for symbolic regression applications in the physical sciences.