{"id":"W1971138856","doi":"10.1007/s00158-005-0523-3","title":"Hybrid evolutionary algorithm and application to structural optimization","year":2005,"lang":"en","type":"article","venue":"Structural and Multidisciplinary Optimization","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mathematical optimization; Truss; Evolutionary algorithm; Convergence (economics); Computer science; Local optimum; Algorithm; Benchmarking; Heuristic; Genetic algorithm; Global optimization; Engineering design process; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001111916,0.0003745874,0.0002687383,0.0002828117,0.0008735905,0.0001850624,0.0003335143,0.0001021169,0.00002469528],"category_scores_gemma":[0.00004650353,0.000354413,0.00004417664,0.0004840818,0.0001300638,0.002225072,0.0005614812,0.0001657166,0.00000698312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001644375,"about_ca_system_score_gemma":0.00004514157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001425657,"about_ca_topic_score_gemma":0.00000280457,"domain_scores_codex":[0.9978147,0.000075374,0.0004375305,0.0009565134,0.0003199053,0.0003959789],"domain_scores_gemma":[0.9987113,0.00005907498,0.0001944414,0.0004064519,0.0003214187,0.0003072641],"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":[0.00001692417,0.00001010769,0.0001173968,0.00001058233,0.00001040281,0.000001876136,0.0004600446,0.8721667,0.00007407687,0.001151247,0.00002039494,0.1259603],"study_design_scores_gemma":[0.0007962965,0.00009925744,0.004213208,0.00001410965,0.000019686,0.0001809218,0.00006629858,0.9929001,0.0001936245,0.0009661308,0.00008622629,0.0004640914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004880775,0.000311186,0.991708,0.001473236,0.0002889344,0.0008637555,0.00004208539,0.0003373024,0.00009475778],"genre_scores_gemma":[0.1109817,0.0001105482,0.8880747,0.000158833,0.0002232137,0.00007082194,0.0001959451,0.00003037786,0.0001538772],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1254962,"threshold_uncertainty_score":0.9998908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005438449798877566,"score_gpt":0.2551655044098465,"score_spread":0.249727054610969,"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."}}