{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006649509,0.001280074,0.00131061,0.001202728,0.0005697848,0.001138686,0.001650643,0.001644187,0.001738324],"category_scores_gemma":[0.001258927,0.0004864814,0.001495362,0.001312721,0.000681374,0.001306814,0.001090651,0.001571367,0.000796383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005747007,"about_ca_system_score_gemma":0.001301521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002416435,"about_ca_topic_score_gemma":0.002663701,"domain_scores_codex":[0.9994926,0.0001613238,0.00003623441,0.00007967775,0.0001872619,0.00004284087],"domain_scores_gemma":[0.99975,0.0001184254,0.00003948866,0.00002417586,0.00005254437,0.00001547941],"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":[0.0001072129,0.0001229103,0.001016114,0.0004643934,0.0003063142,0.0001217601,0.0001121676,0.7438874,0.01000036,0.03129195,0.004509212,0.2080602],"study_design_scores_gemma":[0.00004464193,0.00008367831,0.0002104333,0.00004589949,0.00005404275,0.0001247359,0.0000215414,0.9771025,0.00177773,0.006812147,0.01369629,0.00002628539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006074084,0.002111549,0.9871942,0.000168702,0.0001128009,0.00007801903,0.0000515117,0.0003831083,0.003825944],"genre_scores_gemma":[0.1364885,0.002568158,0.853385,0.0004258367,0.0001241078,0.0007104392,0.0003583327,0.000287327,0.005652344],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002416435,"threshold_uncertainty_score":0.005815208,"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."}}