{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003225623,0.0002800048,0.000202969,0.0003186946,0.0004654153,0.0002579566,0.0005689875,0.0001208945,0.00001017154],"category_scores_gemma":[0.0001555763,0.0002712777,0.0001008088,0.0008940303,0.00009762395,0.0008779431,0.00007019703,0.0001407237,0.00005096722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004481707,"about_ca_system_score_gemma":0.0001179457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001864435,"about_ca_topic_score_gemma":0.000007318457,"domain_scores_codex":[0.9977318,0.00008581003,0.0004003298,0.00105741,0.0003553465,0.0003693149],"domain_scores_gemma":[0.9979816,0.00006875319,0.0002259698,0.0008699315,0.0006813274,0.0001723845],"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":[0.00002109755,0.0007457524,0.000009954624,0.000007058018,0.00001757372,5.06028e-7,0.001051934,0.9841736,0.0002015531,0.01215326,0.00002846105,0.001589233],"study_design_scores_gemma":[0.001779135,0.0002327407,0.0003469498,0.000007445204,0.00001311646,0.000004906249,0.0006611958,0.9920717,0.004323874,0.0001955328,0.00002780024,0.0003355471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000364069,0.000006771547,0.9961581,0.0001291211,0.0003874107,0.001591558,0.00004280408,0.0006378636,0.0006823558],"genre_scores_gemma":[0.1661084,0.00000380864,0.8325534,0.0001924623,0.00006566248,0.0003495461,0.0002588675,0.00003418103,0.0004336585],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1657443,"threshold_uncertainty_score":0.999974,"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."}}