GPU fitness evaluation for GP-GOMEA boosts throughput, improves benchmark results especially on large datasets, and allows reliable regression of large Feynman equations within hours.
arXiv preprint arXiv:2505.01262 (2025)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Genetic programming evolves interpretable feature sets or full survival tree structures, improving performance over standard induction on two real-world datasets at two tree depths.
citing papers explorer
-
GP-GOMEA with GPU-Based Fitness Evaluations: Design and Performance Analysis
GPU fitness evaluation for GP-GOMEA boosts throughput, improves benchmark results especially on large datasets, and allows reliable regression of large Feynman equations within hours.
-
Evolving Features vs Evolving Entire Trees with GP for Interpretable Survival Analysis
Genetic programming evolves interpretable feature sets or full survival tree structures, improving performance over standard induction on two real-world datasets at two tree depths.