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GPU acceleration speeds up GP-GOMEA symbolic regression

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a GPU-accelerated version of GP-GOMEA, an evolutionary algorithm for symbolic regression. This new approach significantly increases the speed of fitness evaluations, allowing for more complex problems and larger datasets to be tackled. The enhanced performance enables GP-GOMEA to discover smaller, more interpretable models and provides new insights into how expression structure affects search difficulty. AI

IMPACT Accelerates symbolic regression capabilities, potentially enabling more efficient discovery of interpretable models in scientific research.

RANK_REASON Academic paper detailing a new method for an existing algorithm. [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 acceleration speeds up GP-GOMEA symbolic regression

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Academic paper detailing a new method for an existing algorithm. [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) · Peter A. N. Bosman ·

    GP-GOMEA with GPU-Based Fitness Evaluations: Design and Performance Analysis

    GP-GOMEA is a state-of-the-art evolutionary algorithm for symbolic regression, known for discovering small and interpretable models. However, its computational cost remains substantial, limiting its applicability to larger datasets and more complex target expressions. In contrast…

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