{"id":"W4249796148","doi":"10.1115/detc2004-57194","title":"An Efficient Pareto Set Identification Approach for Multi-Objective Optimization on Black-Box Functions","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Mathematical optimization; Pareto principle; Computer science; Robustness (evolution); Black box; Multi-objective optimization; Set (abstract data type); Computation; Identification (biology); Convergence (economics); Algorithm; Mathematics; Artificial intelligence","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":[0.00206991,0.001134098,0.001113829,0.001358112,0.0007356798,0.0007211795,0.001092516,0.001024603,0.002971719],"category_scores_gemma":[0.002219833,0.0005584086,0.001213967,0.0008561062,0.001007793,0.001096425,0.001653733,0.001529995,0.0007659635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00064694,"about_ca_system_score_gemma":0.001296593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009139925,"about_ca_topic_score_gemma":0.001122724,"domain_scores_codex":[0.9991898,0.0002847205,0.00002442593,0.0000668154,0.0003869962,0.00004730508],"domain_scores_gemma":[0.9992761,0.0004157452,0.00004486572,0.00007842867,0.0001593125,0.00002556284],"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":[0.000037051,0.00006477525,0.0001792139,0.0001134112,0.0000364562,0.00005911955,0.0000719586,0.7957212,0.006181658,0.08203621,0.0009288112,0.1145702],"study_design_scores_gemma":[0.000005762045,0.00002725969,0.00003354223,0.00001133717,0.000004248021,0.00001674932,0.000005222003,0.9829379,0.001322454,0.01426522,0.001363391,0.000006998016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008593391,0.00003015128,0.9981171,0.00001299167,0.000005563074,0.00001977058,0.000004186033,0.00004686474,0.0009041018],"genre_scores_gemma":[0.1008391,0.0002254386,0.8950329,0.00007657766,0.00002518969,0.0004204348,0.00005651041,0.0000943116,0.00322958],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002971719,"threshold_uncertainty_score":0.01094681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03318662723341084,"score_gpt":0.3035175838474078,"score_spread":0.270330956613997,"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."}}