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New methods improve genetic programming for symbolic regression

By PulseAugur Editorial · [2 sources] ·

Researchers have developed new methods to improve symbolic regression using genetic programming by employing description length (DL) and fractional Bayes factor (FBF) criteria. These criteria help select compact and generalizable expressions, mitigating issues like overfitting and structural bloat, especially in the presence of noisy data. The study compared different search and selection strategies, finding that post-selection with DL/FBF enhances test performance over traditional AIC/BIC baselines, while using DL/FBF directly as a fitness function can lead to premature convergence. AI

IMPACT Introduces refined techniques for model selection in genetic programming, potentially improving the accuracy and generalizability of symbolic regression models.

RANK_REASON Academic paper on a novel method for symbolic regression.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Gabriel Kronberger, Fabricio Olivetti de Franca, Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira ·

    Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions

    arXiv:2605.22374v1 Announce Type: cross Abstract: Symbolic regression with genetic programming (GPSR) may suffer from overfitting and structural bloat, especially when noise is present. In this paper we evaluate description length (DL) and fractional Bayes factor (FBF) criteria a…

  2. arXiv stat.ML TIER_1 English(EN) · Pedro G. Ferreira ·

    Guiding Multi-Objective Genetic Programming with Description Length Improves Symbolic Regression Solutions

    Symbolic regression with genetic programming (GPSR) may suffer from overfitting and structural bloat, especially when noise is present. In this paper we evaluate description length (DL) and fractional Bayes factor (FBF) criteria as principled, data-efficient alternatives to heuri…

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