{"id":"W209715872","doi":"","title":"An Evolutionary Race: A Comparison of Genetic Algorithms and Particle Swarm Optimization for Training Neural Networks.","year":2004,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Particle swarm optimization; Computer science; Artificial neural network; Train; Artificial intelligence; Genetic algorithm; Evolutionary algorithm; Evolutionary computation; Task (project management); Training (meteorology); Machine learning; Track (disk drive); Engineering","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":[0.0003299284,0.0001222484,0.000231483,0.00009040794,0.0001623067,0.0001168011,0.0003893632,0.00005940324,0.00002185829],"category_scores_gemma":[0.0001057072,0.0001175229,0.00003859944,0.0004606867,0.00008160352,0.0004694771,0.00009654736,0.00008406452,8.440666e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003443633,"about_ca_system_score_gemma":0.00009154352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002017943,"about_ca_topic_score_gemma":0.000002425614,"domain_scores_codex":[0.9984826,0.00008613407,0.0004137351,0.000378967,0.0003141967,0.0003243308],"domain_scores_gemma":[0.9989464,0.0001441021,0.0001162522,0.0003418596,0.0002627716,0.0001886061],"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":[0.000009803091,0.0001174323,0.0004511748,0.00000961928,0.000009196833,0.000001097451,0.0006267156,0.9552168,0.00002690657,0.003096408,0.00002716379,0.04040775],"study_design_scores_gemma":[0.0009527824,0.0003330571,0.00138995,0.000006316268,0.000008425726,0.000009047601,0.0001434747,0.9962757,0.0002706252,0.0004633996,0.00001646185,0.0001307873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008706132,0.0003205856,0.9898208,0.0004558898,0.0001491559,0.0004170081,0.000003060982,0.00009977272,0.00002759588],"genre_scores_gemma":[0.4191557,0.00001608688,0.5806898,0.00004077217,0.00004029713,0.00002594654,0.000007147075,0.000008201082,0.00001603235],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4104495,"threshold_uncertainty_score":0.4792444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05340394810975989,"score_gpt":0.336720100568199,"score_spread":0.2833161524584391,"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."}}