{"id":"W4392943001","doi":"10.1109/icmla58977.2023.00112","title":"A New Self-Adaptive Hybrid Approach Based on History-Driven Methods for Improving Metaheuristics","year":2023,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Metaheuristic; Computer science; Artificial intelligence","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":[0.0007507206,0.0006784271,0.0005973826,0.001016441,0.0003550138,0.0007810579,0.001640326,0.0008218056,0.001512662],"category_scores_gemma":[0.001177691,0.0004192273,0.0007961292,0.0006642583,0.000506389,0.0009664163,0.0009973951,0.0006710978,0.0002741095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004843502,"about_ca_system_score_gemma":0.0007957989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001953546,"about_ca_topic_score_gemma":0.002113155,"domain_scores_codex":[0.9996197,0.0001039924,0.00002109471,0.00006015116,0.0001601425,0.00003488783],"domain_scores_gemma":[0.9995658,0.0001854677,0.00005145112,0.00005850973,0.0001049844,0.0000337997],"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":[0.00008183465,0.0001151821,0.001703138,0.0001833636,0.0001953325,0.0001045366,0.0001632883,0.780568,0.01509158,0.02097157,0.001465255,0.1793569],"study_design_scores_gemma":[0.00001544143,0.00005845368,0.0001465996,0.000007645132,0.00001644289,0.00002536385,0.000009089867,0.9939193,0.001312515,0.002426013,0.002053765,0.000009406122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01613958,0.0005185254,0.9795228,0.0001112788,0.0000773397,0.0000617346,0.00002402168,0.0004177208,0.003126896],"genre_scores_gemma":[0.4763387,0.0005726959,0.517746,0.0002611012,0.00009808211,0.0003572453,0.0001358489,0.0001744001,0.004315928],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001953546,"threshold_uncertainty_score":0.005060434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0715653130843526,"score_gpt":0.3419375089184786,"score_spread":0.270372195834126,"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."}}