{"id":"W4389257879","doi":"10.1007/978-3-031-46866-7_5","title":"Optimization","year":2023,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on mechanical engineering","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Bayesian optimization; Computer science; Process (computing); Machine learning; Particle swarm optimization; Hyperparameter optimization; Artificial intelligence; Metaheuristic; Multi-swarm optimization; Engineering optimization; Process optimization; Inference; Optimization problem; Mathematical optimization; Algorithm; Engineering; Support vector machine; Mathematics","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":[0.0004326169,0.001256135,0.001056371,0.0006873865,0.0004969789,0.001644178,0.0008944272,0.0009365108,0.08148844],"category_scores_gemma":[0.001764237,0.0004641177,0.0007800136,0.0008183027,0.0006784652,0.001227109,0.001263178,0.001695665,0.02379628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007852311,"about_ca_system_score_gemma":0.000646593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001022305,"about_ca_topic_score_gemma":0.001129442,"domain_scores_codex":[0.9995196,0.0001044891,0.00001551946,0.0001348021,0.00018066,0.00004504349],"domain_scores_gemma":[0.9997186,0.0001002121,0.00001893337,0.00008141547,0.00005979779,0.00002097219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007839373,0.00007589208,0.0002005079,0.0002858809,0.00007864721,0.00004322972,0.00003596843,0.07473594,0.001877866,0.4371445,0.1746788,0.3107644],"study_design_scores_gemma":[0.00004701326,0.00006310165,0.000376495,0.0001196246,0.00004089759,0.0001125037,0.00003308459,0.1615519,0.002744244,0.4734442,0.3614434,0.00002348648],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.002945837,0.004696212,0.4274514,0.002054622,0.001521766,0.00008981138,0.001033565,0.001388113,0.5588186],"genre_scores_gemma":[0.1281731,0.00502144,0.1418767,0.001846032,0.001295411,0.0003678865,0.00292783,0.002675552,0.715816],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.08148844,"threshold_uncertainty_score":0.272606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01088378025746942,"score_gpt":0.1830527667811313,"score_spread":0.1721689865236619,"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."}}