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New research explores genetic programming for symbolic regression · 2 sources tracked

By PulseAugur Editorial · [4 sources] ·

Two recent arXiv papers explore genetic programming (GP) for symbolic regression (SR). One study, "Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression," found that different random initialization methods, including those seeded with optimized solutions, yielded no significant differences in accuracy or model complexity for GP-based SR. The other paper, "Evolutional Math," introduces a new open-source system designed to produce compact and interpretable formulas for small, wide datasets, a regime where traditional GP often fails by overfitting. This system employs cross-validation for fitness, a multi-island architecture, structural deduplication, and numerical constant refinement. AI

IMPACT These papers explore methods for improving symbolic regression, a technique that can yield interpretable models, potentially aiding in scientific discovery and complex system analysis.

RANK_REASON Two arXiv papers on genetic programming for symbolic regression.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New research explores genetic programming for symbolic regression · 2 sources tracked

COVERAGE [4]

  1. arXiv cs.LG TIER_1 English(EN) · Lukas Kammerer, Gabriel Kronberger, Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira, Stephan Winkler ·

    Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression

    arXiv:2606.31990v1 Announce Type: cross Abstract: We analyze the effect of optimizing the initial population of genetic programming (GP) for symbolic regression (SR) on the accuracy and complexity of solutions. We compare three well-established random initialization methods as we…

  2. arXiv cs.LG TIER_1 English(EN) · Stephan Winkler ·

    Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression

    We analyze the effect of optimizing the initial population of genetic programming (GP) for symbolic regression (SR) on the accuracy and complexity of solutions. We compare three well-established random initialization methods as well as initialization with small optimized solution…

  3. arXiv cs.AI TIER_1 English(EN) · Artem Andrianov (Cyntegrity Germany GmbH, Hofheim am Taunus, Germany) ·

    Evolutional Math: Cross-Validated Island-Model Genetic Programming for Interpretable Symbolic Regression on Small, Wide Datasets

    arXiv:2606.28381v1 Announce Type: cross Abstract: Symbolic regression via genetic programming routinely fails on small, wide datasets - a regime common in clinical-trial monitoring, biostatistics, and engineering pilot studies - by converging on bloated, overfit expressions that …

  4. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Artem Andrianov ·

    Evolutional Math: Cross-Validated Island-Model Genetic Programming for Interpretable Symbolic Regression on Small, Wide Datasets

    Symbolic regression via genetic programming routinely fails on small, wide datasets - a regime common in clinical-trial monitoring, biostatistics, and engineering pilot studies - by converging on bloated, overfit expressions that exploit correlation rather than prediction. We pre…

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