{"id":"W3206780878","doi":"10.1115/omae2021-62304","title":"Adaptive Constraint Handling in Optimization of Complex Structures by Using Machine Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Constraint (computer-aided design); Convergence (economics); Multi-objective optimization; Mathematical optimization; Exploit; Artificial neural network; Task (project management); Artificial intelligence; Pareto principle; Optimization problem; Genetic algorithm; Machine learning; Algorithm; Engineering; Mathematics","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":[0.0008367395,0.0006574503,0.0007170468,0.0004823948,0.0003112422,0.0006671434,0.0006809621,0.0007784991,0.001042268],"category_scores_gemma":[0.001456187,0.000357021,0.0005245713,0.0005333641,0.0005720117,0.000554653,0.0005950863,0.0006624266,0.0001795088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004806249,"about_ca_system_score_gemma":0.0005873462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002586717,"about_ca_topic_score_gemma":0.002478866,"domain_scores_codex":[0.9996044,0.000174379,0.00001810173,0.00004191845,0.0001309995,0.00003027297],"domain_scores_gemma":[0.9993137,0.0004331631,0.00009558341,0.00005549016,0.00008862872,0.00001349116],"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":[0.00001274283,0.00001781633,0.0001909135,0.0000287633,0.0000167692,0.00002182794,0.00001330491,0.9764023,0.001717417,0.002036922,0.0001045621,0.01943676],"study_design_scores_gemma":[0.000001836986,0.000009093643,0.00003847901,0.000003219637,0.000001534444,0.000003811603,0.000001589059,0.9988087,0.0003756471,0.0005964923,0.0001582016,0.000001458246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02074933,0.000184843,0.9765347,0.00008068916,0.0000135564,0.0000309421,0.00001230633,0.0001651643,0.002228314],"genre_scores_gemma":[0.7270555,0.0002399449,0.2703449,0.00009451513,0.0000278932,0.0001960251,0.00005322579,0.00006275145,0.00192526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002586717,"threshold_uncertainty_score":0.005143344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05955576062711624,"score_gpt":0.3081879165762,"score_spread":0.2486321559490837,"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."}}