{"id":"W4414156966","doi":"10.3390/math13182909","title":"Machine Learning for Enhancing Metaheuristics in Global Optimization: A Comprehensive Review","year":2025,"lang":"en","type":"review","venue":"Mathematics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Metaheuristic; Flexibility (engineering); Hyper-heuristic; Key (lock); Search-based software engineering; Global optimization; Resource (disambiguation)","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":[0.001285521,0.0005887965,0.002986551,0.0003807306,0.0001471771,0.0002528187,0.001788306,0.000242281,0.00006434473],"category_scores_gemma":[0.004853181,0.0005159169,0.000485915,0.002635004,0.00004500883,0.0001458783,0.000792996,0.0005456277,0.00004433955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003237023,"about_ca_system_score_gemma":0.0008661026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003645323,"about_ca_topic_score_gemma":0.00000379197,"domain_scores_codex":[0.9957165,0.0005183509,0.001844851,0.0007508383,0.0006380563,0.0005314453],"domain_scores_gemma":[0.994796,0.002614024,0.0008180792,0.001066391,0.0005708272,0.000134652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[6.304476e-7,0.0001387744,1.558975e-7,0.4279386,0.0001378524,0.0000254967,0.00004250671,0.01188448,3.727323e-9,0.01470114,0.0008707144,0.5442597],"study_design_scores_gemma":[0.0001465513,0.00002270857,9.387086e-9,0.05090835,0.0002896008,0.00003035533,0.000002917555,0.4757383,5.590493e-8,0.0005562263,0.4720164,0.0002885583],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[1.397074e-10,0.4995035,0.4979947,0.0000498405,0.0001455759,0.001569097,0.00003207695,0.0000767294,0.0006284441],"genre_scores_gemma":[2.257072e-9,0.5422677,0.456507,0.00007087778,0.00002614238,0.0002988919,0.0001039606,0.00002252725,0.0007028386],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.5439711,"threshold_uncertainty_score":0.9997292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07026562065125108,"score_gpt":0.3910868932276872,"score_spread":0.3208212725764361,"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."}}