{"id":"W2799537251","doi":"10.1139/tcsme-2002-0012","title":"EXHAUSTIVE SEARCH APPROXIMATIONS IN DESIGN OPTIMIZATION: AN ALGORITHMIC IMPLEMENTATION","year":2002,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Convergence (economics); Extension (predicate logic); Computer science; Sensitivity (control systems); Variance (accounting); Optimization problem; Algorithm; Mathematics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005425209,0.000108656,0.0001328908,0.000159219,0.0002546102,0.00007660027,0.0006302111,0.00008938312,0.000165505],"category_scores_gemma":[0.00003219899,0.0001074921,0.0001627218,0.001022997,0.00002728015,0.0004027568,0.00001347873,0.0002109776,0.000001220701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003960168,"about_ca_system_score_gemma":0.000207358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002359492,"about_ca_topic_score_gemma":0.001881686,"domain_scores_codex":[0.9988092,0.00004394338,0.0002836736,0.0002387779,0.0002812679,0.0003431897],"domain_scores_gemma":[0.9991147,0.000113073,0.00003708154,0.0003629106,0.000163238,0.0002089937],"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":[5.861605e-7,0.00002518236,5.421235e-7,0.00001635959,0.00002286385,2.038782e-7,0.0005625816,0.9905411,0.0000952418,0.004067027,0.00004144162,0.004626845],"study_design_scores_gemma":[0.0003880151,0.00004752181,0.00001027434,0.000009856196,0.0000111587,0.000003463113,0.000138179,0.9976495,0.001456324,0.0001134855,0.00006684895,0.000105365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00004176755,0.00002031881,0.9979329,0.0009802785,0.0001378641,0.0007932531,0.00003585802,0.00004398114,0.00001380657],"genre_scores_gemma":[0.1157625,0.00001735101,0.883901,0.00004904472,0.00001929748,0.0001573664,0.000006623774,0.00001744917,0.0000693978],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1157207,"threshold_uncertainty_score":0.4383402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04780455940491037,"score_gpt":0.275645289553464,"score_spread":0.2278407301485537,"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."}}