{"id":"W4297769474","doi":"10.5267/j.dsl.2022.6.003","title":"An efficient hybrid genetic algorithm for solving truncated travelling salesman problem","year":2022,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Travelling salesman problem; Tree traversal; Genetic algorithm; Heuristic; Mathematical optimization; Christofides algorithm; Computer science; 2-opt; Lin–Kernighan heuristic; Bottleneck traveling salesman problem; Metaheuristic; Algorithm; Selection (genetic algorithm); Mathematics; Artificial intelligence","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":[0.0004461794,0.000765402,0.0008289021,0.0008248963,0.0004085267,0.0006980305,0.001253654,0.001154085,0.001829864],"category_scores_gemma":[0.0008597751,0.0002994878,0.0006743975,0.0009235716,0.0004271996,0.0005960827,0.0005919769,0.0006904527,0.0003069452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005955532,"about_ca_system_score_gemma":0.001337015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006455909,"about_ca_topic_score_gemma":0.004817967,"domain_scores_codex":[0.9996958,0.00008497959,0.00001290207,0.00006295292,0.00009470733,0.00004872429],"domain_scores_gemma":[0.9998038,0.00009894503,0.0000232957,0.00001399384,0.00004679719,0.00001321902],"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":[0.00005855404,0.0000605878,0.0005380972,0.00007256751,0.00005889926,0.0001179485,0.00006140205,0.9034104,0.003874071,0.01146303,0.001433072,0.07885134],"study_design_scores_gemma":[0.00001564152,0.00003563314,0.0000738276,0.000005392101,0.00001131083,0.00003079973,0.00001131736,0.9968728,0.0004049172,0.001693061,0.0008407313,0.000004619147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03004266,0.0004558338,0.9628001,0.000145585,0.00007767971,0.00009676326,0.00006092618,0.000503897,0.0058166],"genre_scores_gemma":[0.4013539,0.0005453795,0.5915087,0.0001981174,0.00004777783,0.0003693673,0.0003259313,0.0001001411,0.005550688],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006455909,"threshold_uncertainty_score":0.01283664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01925898632065237,"score_gpt":0.3016121113861703,"score_spread":0.2823531250655179,"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."}}