{"id":"W3103210112","doi":"","title":"Improving NSGA-II with an Adaptive Mutation Operator","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Operator (biology); Adaptive mutation; Mutation; Evolutionary algorithm; Mathematical optimization; Computer science; Multi-objective optimization; Evolutionary computation; Process (computing); Pareto principle; Mathematics; Genetic algorithm; Biology","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":[0.001041192,0.001081682,0.0006357351,0.0007392013,0.000366139,0.0005956403,0.0009547417,0.0008160166,0.001200037],"category_scores_gemma":[0.003230201,0.0002482673,0.0006391354,0.0006014423,0.000454979,0.0005407927,0.0008850916,0.0009215786,0.0003353755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005359654,"about_ca_system_score_gemma":0.0009903753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00494338,"about_ca_topic_score_gemma":0.004042474,"domain_scores_codex":[0.9992724,0.0002335021,0.00004322372,0.00008107864,0.0003111849,0.00005868606],"domain_scores_gemma":[0.9992951,0.0002719476,0.00008332224,0.00009621918,0.0002175448,0.00003595949],"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":[0.00009213193,0.0001445092,0.00148469,0.00008539374,0.00008780917,0.0001844843,0.0001284586,0.8517928,0.01333393,0.008991517,0.001564488,0.1221098],"study_design_scores_gemma":[0.00003319051,0.00006897229,0.0004244162,0.00001070246,0.00002559428,0.00005484861,0.00001344305,0.9921417,0.002670208,0.001861461,0.002684013,0.0000114355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08132622,0.0004230456,0.909712,0.0002700013,0.0001852076,0.0002092947,0.00005455817,0.0007552994,0.007064421],"genre_scores_gemma":[0.5126981,0.0004241356,0.4803595,0.0002761785,0.00006481772,0.0003247315,0.0001742366,0.0001714495,0.005506793],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00494338,"threshold_uncertainty_score":0.009829223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01575973197607919,"score_gpt":0.2606782161603119,"score_spread":0.2449184841842327,"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."}}