{"id":"W4384562776","doi":"10.1016/j.cor.2023.106344","title":"MPILS: An Automatic Tuner for MILP Solvers","year":2023,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Fonds de recherche du Québec – Nature et technologies; Institut de Valorisation des Données","keywords":"Tuner; Solver; Computer science; Iterated local search; Set (abstract data type); Mathematical optimization; Heuristic; Local search (optimization); Parameter space; Cluster analysis; Iterated function; Metaheuristic; Algorithm; Artificial intelligence; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.000747894,0.00008482351,0.0001045444,0.0002852305,0.0003434262,0.0002069392,0.0002301237,0.00006204328,0.00003838987],"category_scores_gemma":[0.0001223109,0.00008336573,0.00004125742,0.0007093377,0.00007092412,0.0003405412,0.00004626903,0.0001504939,0.0001767157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001041324,"about_ca_system_score_gemma":0.0000595338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002399201,"about_ca_topic_score_gemma":0.00006060709,"domain_scores_codex":[0.9989575,0.00006921768,0.0001748544,0.0001998173,0.0002179077,0.0003807483],"domain_scores_gemma":[0.9991643,0.0001664545,0.000003285127,0.0003167768,0.0002526789,0.00009650765],"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":[0.000001967281,0.00001813354,0.000007503195,0.00005735368,0.0000122965,0.00000119503,0.0007105072,0.9536747,0.00075003,0.001133276,0.03208157,0.01155142],"study_design_scores_gemma":[0.0002166241,0.0000697476,0.0001925751,0.00002513902,0.000002410162,9.42539e-7,0.0002443927,0.9929044,0.0003429065,0.0002636527,0.005640344,0.00009687891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1562417,0.00004497935,0.8386659,0.00116987,0.0007326983,0.001205943,0.00002340761,0.001116246,0.0007992953],"genre_scores_gemma":[0.8970283,0.0002480054,0.09922765,0.0000992846,0.000330676,0.0008077213,0.0005328325,0.00008530224,0.001640229],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7407866,"threshold_uncertainty_score":0.3399556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09402563793458564,"score_gpt":0.3790488157703011,"score_spread":0.2850231778357155,"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."}}