{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003778036,0.000218495,0.000263151,0.0002534984,0.0004767636,0.0003855987,0.0005537715,0.000106933,0.0000987471],"category_scores_gemma":[0.0003578415,0.0002230816,0.0001183017,0.0006613791,0.00009983067,0.0004634171,0.0001894064,0.0002028224,0.0000292822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001865673,"about_ca_system_score_gemma":0.0002381717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004613104,"about_ca_topic_score_gemma":1.815841e-7,"domain_scores_codex":[0.9976436,0.0001872681,0.0004949978,0.000612898,0.0005257182,0.0005355529],"domain_scores_gemma":[0.9978006,0.000710314,0.0002119491,0.0003478908,0.0007334107,0.000195834],"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":[0.000005664463,0.0001161436,0.0001167432,0.00001933349,0.00003951594,0.000001513577,0.000136395,0.8151875,0.00006020966,0.004092641,0.0002780548,0.1799463],"study_design_scores_gemma":[0.001008333,0.0001510798,0.0004465952,0.00003554694,0.00001046556,0.000006619528,0.0000346589,0.9974413,0.00005862327,0.0005789132,0.00001942966,0.0002084279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001157639,0.00002009354,0.9972575,0.0002881721,0.0006491626,0.001027929,0.00001910562,0.000494915,0.0001273148],"genre_scores_gemma":[0.3008336,4.169083e-7,0.6987097,0.0000716229,0.0002206654,0.00007032348,0.00004361983,0.00001822886,0.00003181032],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3007179,"threshold_uncertainty_score":0.9097005,"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."}}