{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002854409,0.0002355731,0.0002445444,0.0008563497,0.0005842081,0.002342513,0.002432088,0.00004644833,0.00002116601],"category_scores_gemma":[0.001012399,0.00020425,0.0001304034,0.002644606,0.0003253138,0.001154296,0.0004354101,0.0002408678,0.0001301534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001481227,"about_ca_system_score_gemma":0.0002709271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001437989,"about_ca_topic_score_gemma":7.143063e-7,"domain_scores_codex":[0.995351,0.00006514773,0.0006113728,0.001367143,0.001748985,0.0008563232],"domain_scores_gemma":[0.9969587,0.001384377,0.00008104048,0.0008521578,0.0003909031,0.000332848],"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":[0.00000333162,0.00002183186,0.00003129492,0.00001995654,0.00001073098,0.00008844723,0.000356119,0.1542575,0.01135438,0.0003005837,0.001433917,0.8321218],"study_design_scores_gemma":[0.0003884613,0.00004584535,0.0001942163,0.00009258982,0.000005029601,0.00005790717,0.000008799429,0.9893056,0.003357679,0.001245626,0.005039638,0.0002586668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02179473,0.000193998,0.9736559,0.00179329,0.001430313,0.0007332545,0.00001938094,0.0003489202,0.00003016085],"genre_scores_gemma":[0.1107385,0.00002431117,0.8880438,0.000908858,0.000146892,0.00004144255,0.000005865438,0.00002804608,0.00006232026],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.835048,"threshold_uncertainty_score":0.9986932,"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."}}