{"id":"W1498455135","doi":"10.1109/icec.1994.350017","title":"Improving a vehicle routing heuristic through genetic search","year":2002,"lang":"en","type":"article","venue":"","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vehicle routing problem; Computer science; Heuristic; Genetic algorithm; Routing (electronic design automation); Mathematical optimization; Computer network; Artificial intelligence; Machine learning; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.001241163,0.0009905017,0.0009216772,0.001722504,0.0007693168,0.001168436,0.001096653,0.001558359,0.002002462],"category_scores_gemma":[0.003367168,0.0005706634,0.0008553605,0.001312203,0.0009879387,0.0009457524,0.001019744,0.0009707684,0.0005697384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001306628,"about_ca_system_score_gemma":0.001623538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007683895,"about_ca_topic_score_gemma":0.006828762,"domain_scores_codex":[0.9992852,0.0002325442,0.00002558136,0.0001070284,0.0002465215,0.0001032182],"domain_scores_gemma":[0.9993506,0.0003169082,0.00007355717,0.00008634605,0.0001443986,0.00002829155],"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":[0.00003909838,0.00006103224,0.0004475252,0.00003669763,0.00003873567,0.00007184815,0.00005855313,0.9258398,0.002358386,0.01172702,0.001131811,0.05818947],"study_design_scores_gemma":[0.00002292725,0.00003677226,0.0000926892,0.00001421364,0.0000236609,0.0000264446,0.00001678646,0.9927643,0.0009959067,0.004512527,0.00148339,0.00001040108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05647755,0.0005927575,0.9291532,0.000256946,0.00009808459,0.0001336582,0.00004357495,0.0008363888,0.01240793],"genre_scores_gemma":[0.4450411,0.0006091575,0.5485913,0.0001909045,0.00005531722,0.0002188631,0.0001593211,0.0002344941,0.004899476],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007683895,"threshold_uncertainty_score":0.01527834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0310083192560953,"score_gpt":0.2584753643480404,"score_spread":0.2274670450919451,"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."}}