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Research questionHow can symbolic regression optimize constants efficiently across heterogeneous expression-tree populations on GPUs?In tree-based genetic programming, fitting numerical constants for many structurally different candidate expressions can dominate each generation. GPU frameworks may therefore omit or simplify this step, limiting equation recovery.
Code Generation & Program Synthesis
Machine Learning
Neural and Evolutionary Computing
Latest papersRecent research connected to this question, newest first.Efficient Constant Optimization for Symbolic Regression with GPU-Accelerated Tree-Based Genetic ProgrammingThe evidence concerns a GPU-resident, batched Levenberg–Marquardt solver integrated into EvoGP. It includes throughput measurements on an NVIDIA A100, comparison with Operon on a 64-core EPYC 7763, fp64-reference quality, and recovery results on 18 constructed problems.research paper · Sep 3, 2026
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How can symbolic regression decompose complex equations without relying on brute-force sub-expression search?How can LLM-generated GPU kernels remain performant across hardware, inputs, and programming models?How should GPU implementations be compared fairly when their optimization effort is asymmetric?How can symbolic regression recover compact compositional formulas from noisy observations?
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