{"id":"W2148047235","doi":"10.1504/ijmheur.2014.068904","title":"Nonlinear threshold accepting meta-heuristic for combinatorial optimisation problems","year":2014,"lang":"en","type":"article","venue":"International Journal of Metaheuristics","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Mathematical optimization; Neighbourhood (mathematics); Hill climbing; Simulated annealing; Heuristic; Nonlinear system; Local search (optimization); Algorithm; Filter (signal processing); Tabu search; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001317776,0.001213449,0.001224324,0.001358148,0.0004858447,0.0013862,0.001925847,0.001676076,0.002424716],"category_scores_gemma":[0.003252929,0.0005305146,0.001314494,0.001463686,0.0008950176,0.0009615119,0.0008515165,0.001508181,0.0004196235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001256739,"about_ca_system_score_gemma":0.001271558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002864725,"about_ca_topic_score_gemma":0.002872462,"domain_scores_codex":[0.9993663,0.00027285,0.0000457366,0.00007937699,0.0001659606,0.00006975097],"domain_scores_gemma":[0.9986022,0.001011865,0.0001314845,0.0000837461,0.0001198441,0.00005080751],"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":[0.00004429851,0.00004835182,0.0003870246,0.00008853642,0.0000564984,0.00004751358,0.00004198785,0.9659957,0.00068076,0.006784317,0.0004855045,0.02533939],"study_design_scores_gemma":[0.00001502616,0.00003328677,0.00006064455,0.0000146261,0.00001307343,0.00001451546,0.00001020707,0.995559,0.0002693993,0.003423242,0.0005824295,0.000004578282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04237328,0.001126111,0.9463798,0.0002922645,0.00008788362,0.0002098202,0.0001253807,0.0005484127,0.008857198],"genre_scores_gemma":[0.4714342,0.0008557279,0.5211944,0.0002763367,0.00006899206,0.0007723039,0.0003508769,0.0001858848,0.004861226],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002864725,"threshold_uncertainty_score":0.009118259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03353671793684426,"score_gpt":0.2730317602403623,"score_spread":0.239495042303518,"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."}}