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About this paper
Xuanhao Pan, Chenguang Wang, Chaolong Ying, Ye Xue, Tianshu Yu
Researchers at The Chinese University of Hong Kong, Shenzhen, rigorously evaluated the "Heatmap + MCTS" paradigm for the Traveling Salesman Problem, demonstrating that MCTS configuration significantly impacts solution quality and that a simple, parameter-free prior can match or exceed complex learned heatmaps.
Key insights
- Meticulous MCTS configuration profoundly influences solution quality, with hyperparameters like `Max_Candidate_Num` showing consistent, strong impact across different heatmaps and scales.
- A simple, parameter-free GT-Prior, when coupled with an optimally tuned MCTS, can match or surpass the performance of more sophisticated, learned heatmaps, challenging the notion that increased complexity is the primary path to performance gains.
- External post-processing steps (e.g., sparsification, adaptive softmax) applied to learned heatmaps critically enhance their performance, particularly for large-scale instances.
Summary by alphaXiv.
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