{"id":"W2168924884","doi":"10.1109/cec.2006.1688662","title":"Improving Evolution Strategies through Active Covariance Matrix Adaptation","year":2006,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":145,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"CMA-ES; Adaptation (eye); Evolution strategy; Covariance matrix; Test suite; Computer science; Suite; Covariance; Mathematical optimization; Matrix (chemical analysis); Mutation; Algorithm; Artificial intelligence; Evolutionary computation; Machine learning; Mathematics; Statistics; Test case; Biology; Genetics; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001964797,0.0008945487,0.0008620996,0.0006703844,0.0003719985,0.0008914542,0.001719494,0.001148183,0.001348614],"category_scores_gemma":[0.009179033,0.0004299552,0.0005523123,0.0004948819,0.0007578774,0.001435127,0.001289244,0.001115098,0.0005592934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004440867,"about_ca_system_score_gemma":0.0006885021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009952082,"about_ca_topic_score_gemma":0.0009364028,"domain_scores_codex":[0.9988244,0.0004636654,0.00007384969,0.0002317685,0.0003322043,0.0000741149],"domain_scores_gemma":[0.9964585,0.002117769,0.0002883721,0.0005117282,0.0005501808,0.00007335604],"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":[0.0001045001,0.0001696098,0.002234949,0.0001162074,0.0001667861,0.0001412731,0.000233946,0.6580369,0.02882021,0.04046193,0.001594288,0.2679195],"study_design_scores_gemma":[0.00002971238,0.00007235607,0.0002461149,0.000008553313,0.00002118468,0.00007663767,0.00001119235,0.9854166,0.003399999,0.009299714,0.001400993,0.00001692042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02272176,0.0001822306,0.9748701,0.00009557006,0.00003498854,0.0000360333,0.0000126031,0.0003411793,0.001705605],"genre_scores_gemma":[0.6148521,0.0002681988,0.3807738,0.000283573,0.00007838289,0.0002297259,0.0001130629,0.0002118626,0.003189408],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001964797,"threshold_uncertainty_score":0.01039094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01255610258054151,"score_gpt":0.2521942232550595,"score_spread":0.239638120674518,"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."}}