{"id":"W4407638347","doi":"10.1109/tetci.2025.3537916","title":"Population Stream-Driven Scalable Evolutionary Many-Objective Optimization","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Scalability; Computer science; Population; Evolutionary algorithm; Artificial intelligence; Demography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001580774,0.0002471122,0.0002266545,0.000844272,0.0003668501,0.00009187114,0.0005133963,0.0001223205,0.00005633715],"category_scores_gemma":[0.00003986568,0.0002945142,0.00009402144,0.001841975,0.00007556707,0.0008540661,0.00001443768,0.000366098,0.00002234498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000634302,"about_ca_system_score_gemma":0.0001521569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000104719,"about_ca_topic_score_gemma":0.00003375998,"domain_scores_codex":[0.9979277,0.0001356689,0.0005584611,0.0006760689,0.000403815,0.000298247],"domain_scores_gemma":[0.998704,0.0003406165,0.0001374593,0.0003579571,0.0003965418,0.00006346166],"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":[0.00001494719,0.0002080681,0.0001844829,0.0000118786,0.00002418907,0.000004199848,0.0001987312,0.9274494,0.000003106937,0.03265785,0.00001808323,0.03922508],"study_design_scores_gemma":[0.0002258023,0.00004368407,0.001816609,0.00009942065,0.000007919445,0.000004960994,0.00007048789,0.9713166,0.0005357303,0.02559413,0.00004535758,0.0002393168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002913808,0.0000448766,0.9959325,0.0009837532,0.001257246,0.0003937052,0.00001192026,0.0002437439,0.0008408305],"genre_scores_gemma":[0.4703608,0.00004582948,0.5287394,0.0002144039,0.00002771337,0.00006504868,0.00001745562,0.00001207539,0.000517224],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4700695,"threshold_uncertainty_score":0.9999507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01709219290902881,"score_gpt":0.3029121016931501,"score_spread":0.2858199087841213,"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."}}