{"id":"W179681099","doi":"10.1007/978-3-642-33515-0_55","title":"Rapid Control Selection through Hill-Climbing Methods","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Hill climbing; Climbing; Selection (genetic algorithm); Dimension (graph theory); Random search; Control (management); Model predictive control; Set (abstract data type); Computer science; Mathematical optimization; Machine learning; Mathematics; Algorithm; Artificial intelligence; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001034659,0.0004629562,0.000567314,0.0003507706,0.0001473762,0.0001217581,0.0004888193,0.0003878731,0.00004837502],"category_scores_gemma":[0.0001045093,0.0004613129,0.0001052175,0.0003301119,0.0001721969,0.0006324229,0.00008111136,0.0007170884,0.00002659428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005550419,"about_ca_system_score_gemma":0.00006560626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007472374,"about_ca_topic_score_gemma":0.00001368091,"domain_scores_codex":[0.9978163,0.00007154554,0.0005154485,0.0006038812,0.0003889923,0.0006038026],"domain_scores_gemma":[0.9985983,0.0005284953,0.0001745209,0.0004521558,0.0001467777,0.00009978456],"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":[0.000002790669,0.000002494645,0.000006484389,0.00002735253,0.00001560743,0.000001399665,0.0001514068,0.5867394,0.001285473,0.0007265775,0.000003551888,0.4110375],"study_design_scores_gemma":[0.0004039977,0.00004274404,0.0000120033,0.00018364,0.00002758256,0.00003971596,1.219702e-7,0.9776561,0.00204248,0.01474488,0.004327633,0.0005191442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000004165068,0.004552399,0.9869086,0.00005515347,0.002594952,0.0005138293,0.000003912572,0.0004291083,0.004937854],"genre_scores_gemma":[0.1700116,0.0002307367,0.8276472,0.0003686027,0.00146271,0.00003047421,0.000007660597,0.0001275903,0.0001134702],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4105184,"threshold_uncertainty_score":0.9997839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01590554455681455,"score_gpt":0.2639166353443206,"score_spread":0.248011090787506,"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."}}