{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003149428,0.001507488,0.00130385,0.00165206,0.0005141398,0.001335863,0.001780699,0.002435713,0.002529034],"category_scores_gemma":[0.008809934,0.0005992336,0.0007184449,0.001711305,0.0009612234,0.001866489,0.001147649,0.00164724,0.0005956031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001006996,"about_ca_system_score_gemma":0.001111381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009695836,"about_ca_topic_score_gemma":0.007733613,"domain_scores_codex":[0.9987906,0.0005528887,0.00006226532,0.0001486277,0.0003791829,0.00006647619],"domain_scores_gemma":[0.9971011,0.002233478,0.0001291386,0.0001663358,0.0003076317,0.00006229008],"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":[0.0001668999,0.0001481952,0.001366922,0.0002028398,0.0002278427,0.00007057496,0.0001059041,0.7968377,0.0005621773,0.01277743,0.002594679,0.1849388],"study_design_scores_gemma":[0.00003043344,0.00009579636,0.0002947125,0.00004097803,0.00002493965,0.00002950563,0.000029066,0.9920105,0.0003626703,0.004322314,0.002749057,0.00001013563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02913245,0.005554069,0.9463075,0.000949766,0.000487773,0.0003019417,0.000141075,0.001841408,0.01528395],"genre_scores_gemma":[0.2935178,0.003640356,0.6932232,0.0007347189,0.0002534744,0.0007097511,0.0003977104,0.0007316996,0.006791194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009695836,"threshold_uncertainty_score":0.01927876,"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."}}