{"id":"W4387265244","doi":"10.3390/biomimetics8060470","title":"Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems","year":2023,"lang":"en","type":"article","venue":"Biomimetics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Benchmark (surveying); Metaheuristic; Algorithm; Test suite; Computer science; Suite; Mathematical optimization; Engineering optimization; Test functions for optimization; Imperialist competitive algorithm; Optimization algorithm; Parallel metaheuristic; Evolutionary algorithm; Optimization problem; Test case; Artificial intelligence; Machine learning; Mathematics; Meta-optimization; Multi-swarm optimization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003739616,0.0002672985,0.0002634789,0.0003882443,0.0004729769,0.0002907315,0.0007695059,0.0001524646,0.00002022034],"category_scores_gemma":[0.00008784413,0.0002770173,0.0001452263,0.002175647,0.00006211199,0.0004978485,0.0002447053,0.00009606663,0.00007833666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000940461,"about_ca_system_score_gemma":0.0001958746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003731369,"about_ca_topic_score_gemma":8.505708e-7,"domain_scores_codex":[0.99791,0.00003654321,0.0005112661,0.000672123,0.0003562806,0.0005137591],"domain_scores_gemma":[0.99843,0.0001846667,0.0002261527,0.0006333567,0.0003050944,0.0002207212],"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":[0.000001573311,0.0001306735,0.000006453268,0.00002252986,0.00006469586,0.000002992437,0.0002005394,0.7445437,0.0002931684,0.005967208,0.008549496,0.240217],"study_design_scores_gemma":[0.0006731008,0.000107028,0.00003461211,0.00002482453,0.00004897929,0.000009020434,0.00001833432,0.9866973,0.0004759968,0.002991753,0.008579234,0.0003398101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001201065,0.0002510133,0.9954329,0.001696414,0.0005839007,0.0009756997,0.00006780036,0.0009053088,0.00007491638],"genre_scores_gemma":[0.0003048585,0.0002345295,0.9971145,0.0001153166,0.0003535137,0.0002490379,0.0003921965,0.00004509639,0.001190972],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2421536,"threshold_uncertainty_score":0.9999682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02758115828901661,"score_gpt":0.2594345965594421,"score_spread":0.2318534382704255,"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."}}