{"id":"W2478279759","doi":"","title":"A hybrid genetic algorithm for the Generalized Traveling Salesman Problem","year":2001,"lang":"en","type":"article","venue":"Genetic and Evolutionary Computation Conference","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Travelling salesman problem; Heuristics; Vertex (graph theory); Bottleneck traveling salesman problem; Algorithm; Computer science; Benchmark (surveying); Genetic algorithm; 2-opt; Lin–Kernighan heuristic; Mathematical optimization; Selection (genetic algorithm); Christofides algorithm; Mathematics; Graph; Combinatorics; Theoretical computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003014499,0.0001974551,0.0001915742,0.0001094068,0.0005811701,0.0002868767,0.000584812,0.00004932818,0.00004842245],"category_scores_gemma":[0.00004942672,0.0001639037,0.00006097892,0.000312372,0.0001551123,0.0001667894,0.000186139,0.0001201753,0.00002372259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004038108,"about_ca_system_score_gemma":0.0002885299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000339818,"about_ca_topic_score_gemma":0.000002052287,"domain_scores_codex":[0.9980558,0.0001553544,0.0004173032,0.0005528785,0.0004290146,0.0003896181],"domain_scores_gemma":[0.9984401,0.0003942489,0.0001276703,0.0003160248,0.0005671064,0.00015478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009198517,0.00004592238,0.0001747235,0.00001723327,0.00003967382,0.0000119074,0.0002017494,0.1106589,0.00002812387,0.005364712,0.001329664,0.8821182],"study_design_scores_gemma":[0.0006986858,0.0000886776,0.01317017,0.00001236918,0.00001763857,0.0002398418,0.0000220669,0.9558002,0.00001123037,0.02654195,0.003197373,0.0001998377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002045734,0.001349637,0.9935745,0.001655136,0.0002436836,0.0008191219,0.00001470669,0.0001090814,0.0001883844],"genre_scores_gemma":[0.09705137,0.0008479141,0.9009928,0.000215534,0.0001491033,0.000171394,0.00002686411,0.0000147399,0.0005303497],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8819184,"threshold_uncertainty_score":0.6683798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03526207476254649,"score_gpt":0.2775623342991926,"score_spread":0.2423002595366461,"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."}}