{"id":"W2020700108","doi":"10.1115/1.4001599","title":"On the Performance of the PSP Method for Mixed-Variable Multi-Objective Design Optimization","year":2010,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Benchmark (surveying); Robustness (evolution); Multi-objective optimization; Black box; Pareto principle; Computer science; Closeness; Continuous variable; Continuous optimization; Variable (mathematics); Engineering design process; Set (abstract data type); Optimization problem; Mathematics; Engineering; Artificial intelligence; Multi-swarm optimization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004605559,0.001042873,0.0009561313,0.0007872596,0.0004280666,0.0007712946,0.001000862,0.000991511,0.001990944],"category_scores_gemma":[0.01178406,0.0003172392,0.0006562521,0.0008290443,0.0007692277,0.001053069,0.0009859574,0.00100045,0.0003168411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004822717,"about_ca_system_score_gemma":0.0007998114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00253363,"about_ca_topic_score_gemma":0.001887435,"domain_scores_codex":[0.998136,0.0008972316,0.00006132833,0.0001155354,0.0007192324,0.00007063042],"domain_scores_gemma":[0.9922133,0.00643435,0.0001818618,0.0004023337,0.0006932041,0.00007488531],"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":[0.0002551131,0.00006900938,0.0008949093,0.0001778661,0.00007916236,0.00004910463,0.00004460151,0.8883286,0.003502633,0.005944135,0.0005031691,0.1001517],"study_design_scores_gemma":[0.00001128663,0.00008318151,0.0002165125,0.00001338967,0.000008973191,0.00001783569,0.000005256857,0.9960084,0.00214427,0.001124294,0.000361359,0.00000528779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06473218,0.001721791,0.9217213,0.0002029024,0.00006520119,0.00007586109,0.00005431847,0.0004351534,0.01099135],"genre_scores_gemma":[0.5758205,0.001122454,0.4203231,0.0001208198,0.00004745368,0.000147602,0.0001391499,0.0001592252,0.002119775],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004605559,"threshold_uncertainty_score":0.02435684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03057773855711829,"score_gpt":0.2875124542375179,"score_spread":0.2569347156803996,"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."}}