English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

For the traveling salesman problem (TSP), the existing supervised learningbased algorithms suffer seriously from the lack of generalization ability. Toovercome this drawback, this paper tries to train (in supervised manner) asmall-scale model, which could be repetitively used to build heat maps for TSPinstances of arbitrarily large size, based on a series of techniques such asgraph sampling, graph converting and heat maps merging. Furthermore, the heatmaps are fed into a reinforcement learning approach (Monte Carlo tree search),to guide the search of high-quality solutions. Experimental results based on alarge number of instances (with up to 10,000 vertices) show that, this newapproach clearly outperforms the existing machine learning based TSPalgorithms, and significantly improves the generalization ability of thetrained model.

Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances