{"id":"W2141258159","doi":"10.1109/cec.2006.1688618","title":"The Distribution Genetic Algorithm: Evolving a Population of Distributions","year":2006,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Crossover; Genetic algorithm; Test suite; Population; Computer science; Mutation; Representation (politics); Algorithm; Suite; Binary number; Artificial intelligence; Mathematics; Machine learning; Test case","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":[0.00115369,0.000538209,0.0006408408,0.0006751637,0.0003013179,0.0009146489,0.001736381,0.0008480945,0.001545571],"category_scores_gemma":[0.00277306,0.0002441769,0.0003564427,0.0009332186,0.0009163666,0.001250411,0.001028325,0.0009667967,0.0004295287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005678374,"about_ca_system_score_gemma":0.0009570317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002007121,"about_ca_topic_score_gemma":0.002025098,"domain_scores_codex":[0.9994606,0.0001694838,0.00001565544,0.0001036424,0.0002131095,0.00003746869],"domain_scores_gemma":[0.9994979,0.0002378728,0.00004512063,0.00008323971,0.0001090022,0.00002682272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009015498,0.0001371984,0.002674839,0.0001211006,0.000129764,0.0001478362,0.0002860251,0.4004782,0.01519284,0.1235671,0.004216309,0.4529587],"study_design_scores_gemma":[0.00008177899,0.0001011608,0.0003529621,0.00001673321,0.00003618281,0.0001848814,0.00002735129,0.9558573,0.005175875,0.02200523,0.01613718,0.0000233854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007840256,0.0001087282,0.9893981,0.0001312697,0.00003666143,0.00005169478,0.00002121888,0.0002900546,0.002122073],"genre_scores_gemma":[0.2009866,0.0003894976,0.7919154,0.0002703773,0.00007200066,0.0003704333,0.0001312971,0.0001767077,0.005687731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002007121,"threshold_uncertainty_score":0.00610137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008355095890864766,"score_gpt":0.2514754385048011,"score_spread":0.2431203426139364,"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."}}