{"id":"W2076520672","doi":"10.1023/b:anor.0000039518.73626.a5","title":"GENI Ants for the Traveling Salesman Problem","year":2004,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Travelling salesman problem; Heuristic; Theory of computation; Benchmark (surveying); Mathematical optimization; Ant colony optimization algorithms; Probabilistic logic; Computer science; Ant colony; Bottleneck traveling salesman problem; Set (abstract data type); Nearest neighbour algorithm; Traveling purchaser problem; 2-opt; Mathematics; Algorithm; 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":[0.0009550497,0.0008869371,0.00107968,0.000813762,0.0008461835,0.002259142,0.001400095,0.002450116,0.01027725],"category_scores_gemma":[0.005607933,0.0005191319,0.0007861505,0.001554502,0.001438439,0.002315929,0.001260045,0.003932502,0.001468393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001320831,"about_ca_system_score_gemma":0.001601167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003847504,"about_ca_topic_score_gemma":0.003930409,"domain_scores_codex":[0.9994117,0.0002881761,0.00001929763,0.00008163122,0.000129943,0.0000692383],"domain_scores_gemma":[0.9985247,0.001078058,0.0001171734,0.00007497298,0.0001253753,0.00007984608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002213529,0.00008727082,0.0004108413,0.0003222513,0.00007278854,0.0001896856,0.0001964876,0.3754964,0.0006588831,0.5473084,0.02038803,0.05464767],"study_design_scores_gemma":[0.00005396814,0.00003535067,0.0001766091,0.00003804302,0.00002226109,0.00007139662,0.00005261225,0.5802231,0.0001436649,0.4076477,0.01151935,0.00001600034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0677898,0.01032416,0.7943999,0.008585299,0.001749265,0.000201697,0.0004629265,0.0004410927,0.116046],"genre_scores_gemma":[0.6168115,0.009553143,0.2963283,0.0007265898,0.001734255,0.0003989068,0.0006634123,0.0004673292,0.07331654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01027725,"threshold_uncertainty_score":0.03438079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3811311709820421,"score_gpt":0.4971652540370582,"score_spread":0.1160340830550161,"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."}}