{"id":"W2123876002","doi":"10.5539/cis.v4n2p39","title":"Genetic Algorithms: Concepts, Design for Optimization of Process Controllers","year":2011,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Advanced Control Systems Design","field":"Engineering","cited_by":221,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Genetic algorithm; MATLAB; Process (computing); Control engineering; Heuristic; Meta-optimization; Fuzzy logic; Control theory (sociology); Control (management); Artificial intelligence; Machine learning; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001874406,0.00006233888,0.0001025547,0.0001128514,0.00005501676,0.00003243977,0.0001335667,0.00002059187,0.000002876221],"category_scores_gemma":[0.00001816948,0.00005670455,0.00001240383,0.0001888671,0.0001046879,0.002250564,0.000009538122,0.00001889014,0.000001405787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001593365,"about_ca_system_score_gemma":0.0000308619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.85089e-7,"about_ca_topic_score_gemma":2.086378e-8,"domain_scores_codex":[0.9994605,0.000005949517,0.000241556,0.00006072869,0.000114461,0.0001168567],"domain_scores_gemma":[0.9995605,0.0000322061,0.00006721094,0.00007766412,0.0002186766,0.00004368731],"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":[0.000007638294,0.000001651402,0.00001605473,0.00003945612,0.000003431121,2.847491e-8,0.001597497,0.9741812,0.00005635466,0.0003600509,0.00002731354,0.02370929],"study_design_scores_gemma":[0.0005329804,0.0000666872,0.0003524508,0.00001269644,0.000003078845,0.000002923395,0.00006508496,0.9975355,0.001143028,0.0001057112,0.0001117108,0.00006816565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008364821,0.00005542169,0.9978755,0.000001930103,0.0001825567,0.0004178738,0.000002774254,0.00005888811,0.0005685391],"genre_scores_gemma":[0.6673297,0.00001283457,0.3325717,0.00003265885,0.00001684033,0.00003178132,7.584969e-7,0.000002792857,9.432835e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6664932,"threshold_uncertainty_score":0.2312344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02154108325851707,"score_gpt":0.2380379623967128,"score_spread":0.2164968791381958,"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."}}