Empirical Analysis of Runtime Distributions of Neighbourhood Search for the Graph Coloring Problem — Code and Data

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dc.contributor.authorPeeraya Thapatsuwan
dc.contributor.authorWarattapop Thapatsuwan
dc.contributor.authorChaichana Kulworatit
dc.date.accessioned2026-05-08T19:26:54Z
dc.date.issued2026-4-21
dc.description.abstractCode and experimental data accompanying the paper "Empirical Analysis of Runtime Distributions of Neighbourhood Search for the Graph Coloring Problem." Contains Python and C implementations of four neighbourhood search algorithms (Random Walk, TABUCOL, PARTIALCOL, Ising heuristic), experiment runners, raw result data (300 trials × 4 algorithms × 27 planted-partition instances + 14 DIMACS-equivalent instances), and figure generation scripts.
dc.identifier.doi10.5281/zenodo.19682140
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/20838
dc.publisherZenodo (CERN European Organization for Nuclear Research)
dc.titleEmpirical Analysis of Runtime Distributions of Neighbourhood Search for the Graph Coloring Problem — Code and Data
dc.typeOther

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