{"id":"W4280625329","doi":"10.23919/date54114.2022.9774576","title":"Efficient Traveling Salesman Problem Solvers using the Ising Model with Simulated Bifurcation","year":2022,"lang":"en","type":"article","venue":"2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Travelling salesman problem; Solver; Computer science; Mathematical optimization; Simulated annealing; Benchmark (surveying); Ising model; Spins; Applied mathematics; Mathematics; Algorithm; Physics; Statistical physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.003508424,0.000460369,0.0003969636,0.000775204,0.001361449,0.0009060677,0.001340793,0.00009759406,0.0006440004],"category_scores_gemma":[0.0008729772,0.0004141236,0.00007139197,0.00438033,0.0001850037,0.0006376707,0.0005638485,0.0008206472,0.0002044181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003867507,"about_ca_system_score_gemma":0.001091385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008504065,"about_ca_topic_score_gemma":0.00004997593,"domain_scores_codex":[0.9933775,0.001792179,0.001104262,0.001156126,0.001835773,0.0007341246],"domain_scores_gemma":[0.9957843,0.0007590704,0.0007017704,0.00130053,0.001260645,0.0001936646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002953132,0.0002804781,0.00008709844,0.00003377005,0.00001632549,0.000006937903,0.002956568,0.9758617,0.01667859,0.002190454,0.0003611337,0.001497368],"study_design_scores_gemma":[0.0008551935,0.00006906714,0.0002436935,0.0001112128,0.00002573524,0.00006596486,0.00009067688,0.9953138,0.0003107598,0.0004627587,0.00194646,0.000504636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04462636,0.00003411756,0.951534,0.000847152,0.0001660415,0.001572222,0.00004160254,0.0005437012,0.0006347959],"genre_scores_gemma":[0.5222749,0.00002742742,0.4752325,0.0002874652,0.00003830067,0.0001377947,0.0004805899,0.00007953697,0.001441555],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4776485,"threshold_uncertainty_score":0.9999387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0955018404612106,"score_gpt":0.3027928999542914,"score_spread":0.2072910594930808,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}