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GPU-accelerated solver boosts symbolic regression for genetic programming

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel GPU-resident solver for optimizing constants in symbolic regression via genetic programming. This batched Levenberg-Marquardt solver efficiently handles heterogeneous populations of expression trees, achieving high throughput on NVIDIA A100 hardware. The method significantly improves the ability to recover governing equations compared to standard genetic programming approaches. AI

IMPACT This new method could accelerate the discovery of scientific laws and mathematical models by improving the efficiency of genetic programming techniques.

RANK_REASON Academic paper detailing a new computational method for symbolic regression. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

GPU-accelerated solver boosts symbolic regression for genetic programming

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Academic paper detailing a new computational method for symbolic regression. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Yuntian Chen ·

    Efficient Constant Optimization for Symbolic Regression with GPU-Accelerated Tree-Based Genetic Programming

    Constant optimization refines the numerical coefficients of candidate expressions in tree-based genetic programming for symbolic regression. But its per-generation cost has led modern GPU-accelerated frameworks to omit it or restrict it to lightweight forms. We present a GPU-resi…

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