{"id":"W2013517059","doi":"10.1016/j.ins.2014.04.013","title":"Multi-strategy ensemble artificial bee colony algorithm","year":2014,"lang":"en","type":"article","venue":"Information Sciences","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":260,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ontario Institute of Technology","funders":"Humanities and Social Sciences Youth Foundation, Ministry of Education of the People's Republic of China; Education Department of Jiangxi Province; National Natural Science Foundation of China","keywords":"Benchmark (surveying); Computer science; Mathematical optimization; Artificial bee colony algorithm; Evolutionary algorithm; Set (abstract data type); Process (computing); Population; Algorithm; Local search (optimization); Search algorithm; Artificial intelligence; 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":[0.0009204742,0.0007020808,0.001548454,0.0007691423,0.0007405579,0.0008479543,0.001439767,0.001202108,0.002296877],"category_scores_gemma":[0.001818002,0.0003141878,0.0007548299,0.0008604624,0.000312037,0.001078612,0.0009490675,0.0007558492,0.0004183741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004495924,"about_ca_system_score_gemma":0.0007013408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002235306,"about_ca_topic_score_gemma":0.002814559,"domain_scores_codex":[0.9994799,0.0001575057,0.00002858891,0.00007359243,0.000187479,0.00007279148],"domain_scores_gemma":[0.9993641,0.0002403743,0.00005150479,0.00006193425,0.0002318517,0.00005036334],"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":[0.0001966197,0.0002111799,0.001908139,0.00008293276,0.0002528611,0.0001106889,0.00005886094,0.8224668,0.003691079,0.008134837,0.003506922,0.1593791],"study_design_scores_gemma":[0.000009412244,0.00003080343,0.0001519101,0.000003573184,0.00001676275,0.00001839927,0.000005370782,0.9983741,0.000268424,0.0008254054,0.0002917572,0.000003985931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09833131,0.001561173,0.8795939,0.0004098226,0.0003687492,0.0001183606,0.00009930953,0.0004278445,0.01908949],"genre_scores_gemma":[0.7928898,0.0005006984,0.1988444,0.0002435856,0.0001215326,0.0002132434,0.0002119884,0.00006807722,0.006906664],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002296877,"threshold_uncertainty_score":0.007683754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06196425370856051,"score_gpt":0.3306889462073624,"score_spread":0.2687246924988019,"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."}}