{"id":"W3157047073","doi":"10.22266/ijies2021.0630.46","title":"Ring Toss Game-Based Optimization Algorithm for Solving Various Optimization Problems","year":2021,"lang":"en","type":"article","venue":"International journal of intelligent engineering and systems","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Mathematical optimization; Optimization problem; Particle swarm optimization; Meta-optimization; Multi-swarm optimization; Test functions for optimization; Continuous optimization; Population; Set (abstract data type); Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008593489,0.0001576113,0.0002609535,0.0004144127,0.00005406938,0.0007534134,0.0005433564,0.00007469345,0.00001405667],"category_scores_gemma":[0.0006197635,0.000153526,0.000104035,0.0002676507,0.00001451603,0.0004289871,0.00009489504,0.0001695778,0.000001112442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001637648,"about_ca_system_score_gemma":0.0001937951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000076464,"about_ca_topic_score_gemma":2.359622e-7,"domain_scores_codex":[0.9980251,0.00005264477,0.0007637391,0.0002433506,0.0007004915,0.0002146305],"domain_scores_gemma":[0.9966417,0.0003167866,0.0003347845,0.0001757346,0.002388559,0.0001424477],"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":[0.000004292839,0.00004997873,0.00001612946,0.00005138113,0.0001178107,0.00004110782,0.0001515296,0.9843777,0.0001339401,0.001118001,0.00002697002,0.0139111],"study_design_scores_gemma":[0.0005030275,0.00006927709,0.000005067702,0.0002910118,0.00001217323,0.0003278448,0.00004011048,0.9962119,0.001302212,0.00001761981,0.00107385,0.0001459431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00008655689,0.001238285,0.9954428,0.000222169,0.002728037,0.0001891782,0.000008533701,0.0000478803,0.00003656014],"genre_scores_gemma":[0.03887003,0.0004343916,0.9599875,0.00003503828,0.0004300295,0.00002423701,0.00002210382,0.00002852737,0.0001681504],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03878347,"threshold_uncertainty_score":0.7265183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02216395391227477,"score_gpt":0.2679632357895824,"score_spread":0.2457992818773077,"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."}}