{"id":"W2734645630","doi":"10.1162/evco_a_00215","title":"Leveraging TSP Solver Complementarity through Machine Learning","year":2017,"lang":"en","type":"article","venue":"Evolutionary Computation","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Deutscher Akademischer Austauschdienst","keywords":"Solver; Leverage (statistics); Computer science; Benchmark (surveying); Complementarity (molecular biology); Euclidean geometry; Mathematical optimization; Problem solver; Set (abstract data type); Travelling salesman problem; Parallel computing; Algorithm; Artificial intelligence; Mathematics; Computational science","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":[0.002905632,0.001299632,0.001339929,0.00132067,0.0006065547,0.001528222,0.00170428,0.001361581,0.002770838],"category_scores_gemma":[0.01084912,0.0004673853,0.0007312036,0.001456839,0.001127102,0.001934953,0.001708994,0.001802451,0.0006544723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008118157,"about_ca_system_score_gemma":0.002078843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003035596,"about_ca_topic_score_gemma":0.004281706,"domain_scores_codex":[0.9981511,0.0007321557,0.00008529182,0.0002685448,0.000564967,0.0001978653],"domain_scores_gemma":[0.9949158,0.003561232,0.0004665187,0.0004897384,0.0004453743,0.0001213158],"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":[0.00009664924,0.0001368915,0.001914828,0.000178416,0.00005636161,0.0000619424,0.00003811319,0.8986169,0.001081776,0.009724985,0.002829649,0.08526354],"study_design_scores_gemma":[0.00001047665,0.0000336024,0.0001005341,0.00000858957,0.000006807401,0.00001337156,0.000008808895,0.9962885,0.0005001024,0.002360066,0.0006658383,0.000003298104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2343079,0.003776695,0.7232198,0.001761684,0.0003584524,0.0002799329,0.0003656948,0.004779456,0.03115038],"genre_scores_gemma":[0.762931,0.000705101,0.2313084,0.0005155154,0.0001696008,0.0001959951,0.000659795,0.0003496003,0.003164881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003035596,"threshold_uncertainty_score":0.01536667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04305681618019019,"score_gpt":0.305783714799549,"score_spread":0.2627268986193588,"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."}}