{"id":"W4399634105","doi":"10.5267/j.dsl.2024.4.001","title":"A hybrid genetic-simulated annealing algorithm for multiple traveling salesman problems","year":2024,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Bisha","keywords":"Travelling salesman problem; Simulated annealing; Genetic algorithm; Mathematical optimization; Computer science; 2-opt; Algorithm; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000800561,0.0008063168,0.0009945669,0.0008931112,0.0006010283,0.0007622256,0.001028138,0.001185138,0.001729592],"category_scores_gemma":[0.001118009,0.0004446766,0.00105314,0.001001408,0.000449864,0.000636006,0.0007440796,0.0008146103,0.0003490426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005732925,"about_ca_system_score_gemma":0.0008473006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00366304,"about_ca_topic_score_gemma":0.003557625,"domain_scores_codex":[0.9993976,0.0002223441,0.00002709103,0.00009397544,0.0002024634,0.0000566104],"domain_scores_gemma":[0.9997503,0.0001333358,0.00002452368,0.00002132423,0.00005544879,0.00001500352],"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":[0.00008804137,0.00008744692,0.000462832,0.00008395072,0.0001178002,0.00009814207,0.00009442092,0.8808456,0.006087318,0.01174835,0.001010875,0.09927522],"study_design_scores_gemma":[0.0000229511,0.00006701159,0.0001219299,0.000006178135,0.00001937546,0.0000445851,0.00001104706,0.9945537,0.001000333,0.002418055,0.001726821,0.000008021863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02805915,0.0004475009,0.9652559,0.0001381274,0.00006945796,0.0001069741,0.00003930555,0.0005746042,0.005308912],"genre_scores_gemma":[0.3314001,0.0003297398,0.6630032,0.0001211344,0.00004005066,0.000326846,0.0001285703,0.00009632095,0.004553997],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00366304,"threshold_uncertainty_score":0.007283449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03378102675463943,"score_gpt":0.3191322940027633,"score_spread":0.2853512672481238,"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."}}