{"id":"W2023062871","doi":"10.1108/ijicc-07-2014-0034","title":"Simultaneous knowledge-based identification and optimization of PHEV fuel economy using hyper-level Pareto-based chaotic Lamarckian immune algorithm, MSBA and fuzzy programming","year":2015,"lang":"en","type":"article","venue":"International Journal of Intelligent Computing and Cybernetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Identifier; Identification (biology); Fuzzy logic; Exploit; Pareto principle; Mathematical optimization; Artificial intelligence; Mathematics","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":[0.0007055784,0.0004778443,0.0004495256,0.0005050118,0.000363191,0.0009746667,0.0007032288,0.0007250534,0.001059308],"category_scores_gemma":[0.001515938,0.0002174412,0.0005292537,0.000283882,0.0004788429,0.000505096,0.0007516905,0.0005376511,0.0001309857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005148557,"about_ca_system_score_gemma":0.0008299581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001724512,"about_ca_topic_score_gemma":0.001314872,"domain_scores_codex":[0.9997781,0.00006338704,0.00001131775,0.00004561072,0.00007076665,0.00003085022],"domain_scores_gemma":[0.9996463,0.0001764289,0.00006090387,0.00002236757,0.00007866399,0.00001529132],"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":[0.00003673699,0.00004289058,0.00080316,0.00004884432,0.00003421901,0.00004228257,0.00006107229,0.9611248,0.003005672,0.004528453,0.000211766,0.03006014],"study_design_scores_gemma":[0.00000298738,0.00002384708,0.00008612315,0.000003493271,0.000003378095,0.000007812248,0.000008181656,0.9984653,0.0005181925,0.0007494554,0.0001287487,0.000002429152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07082114,0.0001936967,0.922856,0.0002083241,0.00002207254,0.00005862303,0.00002081562,0.0001261244,0.005693206],"genre_scores_gemma":[0.9172284,0.0000927305,0.080765,0.00007004855,0.00001065075,0.0001452913,0.00003107573,0.00001195116,0.001644852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001724512,"threshold_uncertainty_score":0.003735542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03317434205134592,"score_gpt":0.2994996557404787,"score_spread":0.2663253136891327,"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."}}