{"id":"W1490136960","doi":"10.1007/978-3-540-78761-7_60","title":"Cumulative Step Length Adaptation for Evolution Strategies Using Negative Recombination Weights","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Adaptation (eye); Recombination; Algorithm; Biology; Genetics; Neuroscience","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":[0.001435904,0.0006584395,0.0007693787,0.0007461456,0.0003754577,0.0007480016,0.001806424,0.001262704,0.003032842],"category_scores_gemma":[0.005607196,0.000355504,0.0004680908,0.0007559914,0.0005530493,0.001079419,0.0009714572,0.001334509,0.0005784985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006753203,"about_ca_system_score_gemma":0.0005607941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001363122,"about_ca_topic_score_gemma":0.001755817,"domain_scores_codex":[0.99961,0.0001181562,0.00002381381,0.0000722863,0.0001399303,0.00003585625],"domain_scores_gemma":[0.9981705,0.001130778,0.0001217712,0.0001652704,0.0003445318,0.00006728153],"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":[0.0001349375,0.0001802219,0.0005483859,0.0001298854,0.00006460637,0.00006539973,0.0001369471,0.6755031,0.01166038,0.02535265,0.001837578,0.2843859],"study_design_scores_gemma":[0.00001189442,0.00003406546,0.0001004981,0.00000953692,0.00001135731,0.00002272985,0.000005291214,0.9955145,0.0009324202,0.002667438,0.0006834032,0.000006952046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01580893,0.0004122978,0.979214,0.00007864986,0.00009822581,0.0000533989,0.00001220258,0.0002658425,0.00405642],"genre_scores_gemma":[0.5152355,0.0004124272,0.4731661,0.000193667,0.0001186479,0.0003403027,0.00008611388,0.000334594,0.0101125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003032842,"threshold_uncertainty_score":0.01014584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05761800603408946,"score_gpt":0.3084248999016581,"score_spread":0.2508068938675686,"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."}}