After per-heatmap tuning of MCTS hyperparameters, a simple k-nearest-neighbor heatmap (GT-Prior) matches or beats learned heatmaps on uniform, shifted-distribution, and TSPLIB benchmarks, while search settings alone swing gaps from under 1% to over 90%.
Learning heuristics for the tsp by policy gradient
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Beyond the Heatmap: A Rigorous Evaluation of Component Impact in MCTS-Based TSP Solvers
After per-heatmap tuning of MCTS hyperparameters, a simple k-nearest-neighbor heatmap (GT-Prior) matches or beats learned heatmaps on uniform, shifted-distribution, and TSPLIB benchmarks, while search settings alone swing gaps from under 1% to over 90%.