{"id":"W3082083549","doi":"10.1007/978-3-030-58112-1_13","title":"Simple Surrogate Model Assisted Optimization with Covariance Matrix Adaptation","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Simple (philosophy); CMA-ES; Adaptation (eye); Covariance matrix; Algorithm; Mathematical optimization; Matrix (chemical analysis); Covariance; Artificial intelligence; Covariance function; Mathematics; Statistics","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":[0.0005043345,0.0009014292,0.00121841,0.0002958429,0.000294678,0.0008811106,0.001098306,0.001685378,0.006898871],"category_scores_gemma":[0.001214265,0.0005350306,0.0009608214,0.0007437181,0.0003685779,0.001040451,0.001249795,0.001570847,0.003038466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000210305,"about_ca_system_score_gemma":0.0004352329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005377405,"about_ca_topic_score_gemma":0.001003587,"domain_scores_codex":[0.9996502,0.0001068684,0.00001267257,0.00004522721,0.0001641446,0.00002099473],"domain_scores_gemma":[0.9996775,0.0001018931,0.00001796327,0.0001154052,0.00007002005,0.00001714335],"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":[0.0001604304,0.0001266809,0.0001919519,0.0002026492,0.00009973176,0.0001258112,0.00003359208,0.7791246,0.0202621,0.03871757,0.007959971,0.152995],"study_design_scores_gemma":[0.000006746895,0.00002232724,0.00004192462,0.000005193592,0.000004910685,0.00003357868,0.00000195355,0.9907885,0.001466883,0.005108582,0.002511964,0.00000740317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002070383,0.0001356677,0.9911747,0.00005383108,0.0001107065,0.00001741004,0.00005378438,0.000529011,0.005854509],"genre_scores_gemma":[0.193568,0.0003221827,0.7799497,0.0002226041,0.0001281982,0.0001959565,0.0005027163,0.0006584396,0.0244522],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006898871,"threshold_uncertainty_score":0.02307904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02397385836459957,"score_gpt":0.2611975937309552,"score_spread":0.2372237353663556,"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."}}