{"id":"W4401011606","doi":"10.1007/s00500-024-09896-5","title":"A co-evolutionary algorithm with adaptive penalty function for constrained optimization","year":2024,"lang":"en","type":"article","venue":"Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Verafin (Canada)","funders":"Università degli Studi di Trento","keywords":"Metaheuristic; Mathematical optimization; Penalty method; Computer science; Evolutionary algorithm; Optimization problem; Constrained optimization; Benchmark (surveying); Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006846138,0.0001981711,0.0001993684,0.0002949726,0.0003834082,0.0004387538,0.000386788,0.00007357293,0.0000522952],"category_scores_gemma":[0.0001182051,0.0001760317,0.00007642459,0.0009136057,0.000112889,0.0005146436,0.00009459614,0.0002180881,0.00003599627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001268636,"about_ca_system_score_gemma":0.0004698351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007094271,"about_ca_topic_score_gemma":3.952623e-7,"domain_scores_codex":[0.9979823,0.0001146869,0.0003154541,0.0006593275,0.0005142812,0.0004140074],"domain_scores_gemma":[0.9982954,0.0007136074,0.00008983938,0.0002909767,0.0004771839,0.0001330331],"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":[0.00003545623,0.00005595672,0.00002078081,0.00007598325,0.0001215817,0.00002986759,0.0003593497,0.6951996,0.00001740781,0.0291046,0.002167497,0.2728119],"study_design_scores_gemma":[0.0004740884,0.0003146057,0.0000479849,0.00009081954,0.00001858877,0.00007535675,0.00006370902,0.9963079,0.0000350171,0.0009115994,0.001442327,0.000218014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001931497,0.0002730456,0.9963265,0.0004149371,0.0005271694,0.0006198854,0.00002527684,0.0006991233,0.001094765],"genre_scores_gemma":[0.03947825,0.000005325845,0.9595959,0.0001171814,0.0002886588,0.00003762672,0.00007931863,0.0000297421,0.0003680355],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3011083,"threshold_uncertainty_score":0.7178363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02337172264000821,"score_gpt":0.28811614323647,"score_spread":0.2647444205964618,"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."}}