{"id":"W4313256689","doi":"10.1007/s00158-022-03435-2","title":"Variable functioning and its application to large scale steel frame design optimization","year":2022,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"University of Technology Sydney; National Science Foundation","keywords":"Mathematical optimization; Particle swarm optimization; Heuristics; Multi-swarm optimization; Variable (mathematics); Engineering design process; Engineering optimization; Frame (networking); Process (computing); Metaheuristic; Computer science; Differential evolution; Optimization problem; Continuous optimization; Convergence (economics); Mathematics; Engineering","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":[0.001958678,0.0005610452,0.0004984668,0.0007045547,0.0003229814,0.0004989544,0.0006019401,0.000612235,0.0007835011],"category_scores_gemma":[0.002156422,0.0001877997,0.0003588076,0.0005455529,0.0006814195,0.0004342599,0.0004838624,0.0004883985,0.0000648403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005158222,"about_ca_system_score_gemma":0.0005481813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001552158,"about_ca_topic_score_gemma":0.0008449954,"domain_scores_codex":[0.9995616,0.0002754175,0.00001281617,0.00003462575,0.00008980225,0.00002561146],"domain_scores_gemma":[0.9991949,0.0005977525,0.00005775196,0.0000383621,0.00009278908,0.00001836132],"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":[0.00003065951,0.00002954314,0.000684974,0.00004369383,0.00001459099,0.00004374015,0.00003291563,0.9619042,0.003110384,0.006929267,0.000135479,0.0270405],"study_design_scores_gemma":[0.000004631308,0.00004423174,0.0001398851,0.00000376115,0.000003176656,0.000009606852,0.000006773603,0.9976072,0.0009190054,0.0009539417,0.0003054226,0.000002292384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07354102,0.0001979723,0.9238365,0.00008323559,0.00002316038,0.00004433419,0.00001097639,0.0001556457,0.002107217],"genre_scores_gemma":[0.8168637,0.00011884,0.1823633,0.00002640558,0.00001019867,0.00007554565,0.00002168105,0.00002568006,0.0004946768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001958678,"threshold_uncertainty_score":0.01035863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01009886240194589,"score_gpt":0.2498449909690287,"score_spread":0.2397461285670828,"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."}}