{"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 &amp; Test in Europe Conference &amp; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006719326,0.0008659257,0.0008731335,0.0008475173,0.0005054144,0.0008701121,0.001121516,0.00137858,0.002052848],"category_scores_gemma":[0.002182791,0.0005636109,0.001004348,0.001162953,0.0004060827,0.000871425,0.0007656647,0.001214672,0.0003466939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008395007,"about_ca_system_score_gemma":0.002287811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008924048,"about_ca_topic_score_gemma":0.007732925,"domain_scores_codex":[0.9996513,0.0001389021,0.00002741346,0.00004661515,0.00009727773,0.00003856527],"domain_scores_gemma":[0.9992126,0.000532222,0.00006822366,0.00006502408,0.000093752,0.00002813802],"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":[0.00003042099,0.00004691853,0.0003367261,0.00008431477,0.00005701417,0.00005164342,0.00004090383,0.9587231,0.001165194,0.0104721,0.0009867705,0.02800492],"study_design_scores_gemma":[0.00001135612,0.000006739042,0.0000210389,0.000003078402,0.000003998378,0.000004753982,0.000004763644,0.9973248,0.0002311459,0.001982559,0.0004039303,0.000001740223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03747382,0.0006313159,0.9524941,0.0002908301,0.00007511114,0.0001553705,0.0001574578,0.001135352,0.00758656],"genre_scores_gemma":[0.273515,0.000611424,0.7223486,0.0001198951,0.00004461432,0.0004271699,0.0004575567,0.0001876852,0.002287933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008924048,"threshold_uncertainty_score":0.01774418,"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."}}