{"id":"W2023835690","doi":"10.1080/03052150108940935","title":"MULTIOBJECTIVE DESIGN OPTIMIZATION BASED ON SATISFACTION METRICS","year":2001,"lang":"en","type":"article","venue":"Engineering Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Multi-objective optimization; Solver; Computer science; Pareto principle; Mathematical optimization; Context (archaeology); Implementation; Constraint satisfaction problem; Engineering design process; Mathematics; Engineering; Software engineering; Artificial intelligence; Probabilistic logic","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":[0.003158402,0.001077441,0.0008652222,0.001097741,0.0003726391,0.001360922,0.0007497414,0.000650944,0.0020417],"category_scores_gemma":[0.00384886,0.0003808903,0.0009672075,0.0008629136,0.001061202,0.001196228,0.001277963,0.0009397165,0.0002700522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001412475,"about_ca_system_score_gemma":0.001128834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001277042,"about_ca_topic_score_gemma":0.0008758018,"domain_scores_codex":[0.9973438,0.001326728,0.0001060183,0.000150767,0.0009421022,0.0001304965],"domain_scores_gemma":[0.9984085,0.0008809544,0.000178406,0.0001345657,0.0003382965,0.00005937484],"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":[0.00003290039,0.0000356388,0.0003033896,0.0001135469,0.0000333989,0.00003240883,0.00006778811,0.8009835,0.004364857,0.1565934,0.0005898718,0.03684923],"study_design_scores_gemma":[0.000008262209,0.00004484506,0.00006022042,0.00001429929,0.000005169638,0.00001478718,0.000009704902,0.9648975,0.001080189,0.03234258,0.001515201,0.000007276876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003963171,0.00006787691,0.9940628,0.00005584856,0.0000093355,0.00003164635,0.00001284878,0.00004253133,0.001753968],"genre_scores_gemma":[0.456864,0.0003019682,0.5386563,0.00009138381,0.00002497942,0.0004558855,0.0001295249,0.0001133558,0.003362566],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003158402,"threshold_uncertainty_score":0.01670343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01440897130216926,"score_gpt":0.2311327433332369,"score_spread":0.2167237720310676,"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."}}