{"id":"W2087109584","doi":"10.1007/s00500-013-1090-y","title":"A prediction-based adaptive grouping differential evolution algorithm for constrained numerical optimization","year":2013,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Crossover; Differential evolution; Mathematical optimization; Convergence (economics); Population; Constraint (computer-aided design); Mutation; Evolutionary algorithm; Selection (genetic algorithm); Computer science; Adaptive mutation; Algorithm; Lipschitz continuity; Optimization problem; Mathematics; Genetic algorithm; Artificial intelligence","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":[0.001003552,0.0005736607,0.001136241,0.0005954127,0.0005830267,0.0006525317,0.001952849,0.001428846,0.002511502],"category_scores_gemma":[0.00241228,0.0004628623,0.0004847571,0.0009115869,0.0005915981,0.0008718154,0.001555362,0.001091185,0.0005621087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005402216,"about_ca_system_score_gemma":0.0009710552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003823722,"about_ca_topic_score_gemma":0.00379003,"domain_scores_codex":[0.9996991,0.00007539836,0.00001603695,0.00004933666,0.0001356141,0.00002451003],"domain_scores_gemma":[0.999392,0.0002829824,0.00004567719,0.00006496688,0.0001799506,0.00003451889],"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":[0.0001029409,0.0001028951,0.0005436782,0.00005424647,0.00004839472,0.00004652167,0.0000678968,0.8221535,0.003686822,0.009030785,0.001718034,0.1624442],"study_design_scores_gemma":[0.000005725998,0.00001012668,0.00002452688,0.000001238046,0.000001951276,0.000003150637,0.000001134143,0.999245,0.000132335,0.0003902793,0.0001828422,0.000001606474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01018438,0.0001194934,0.9872306,0.00008743716,0.00007265926,0.00005458289,0.0000184587,0.0002780104,0.001954442],"genre_scores_gemma":[0.2608718,0.0001556141,0.7343598,0.0001662639,0.00006161253,0.0003767342,0.0001159022,0.0001542085,0.003738151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003823722,"threshold_uncertainty_score":0.008401811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02297026447037997,"score_gpt":0.2548097587423979,"score_spread":0.2318394942720179,"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."}}