{"id":"W4383721094","doi":"10.1007/s10462-023-10542-z","title":"AFOX: a new adaptive nature-inspired optimization algorithm","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Metaheuristic; Meta-optimization; Computer science; Mathematical optimization; Multi-swarm optimization; Derivative-free optimization; Particle swarm optimization; Imperialist competitive algorithm; Convergence (economics); Optimization problem; Algorithm; Local optimum; Parallel metaheuristic; Benchmark (surveying); Continuous optimization; Engineering optimization; Optimization 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001431645,0.0002661975,0.0004481094,0.0003402623,0.0002107609,0.0002613101,0.001491318,0.0001544835,0.0005474922],"category_scores_gemma":[0.00110513,0.0002461681,0.0001708215,0.005122401,0.00007962032,0.0006073043,0.0004233963,0.0004497993,0.00372786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008342542,"about_ca_system_score_gemma":0.0003970923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004275079,"about_ca_topic_score_gemma":0.000004661005,"domain_scores_codex":[0.9966167,0.000302856,0.000806426,0.0007675606,0.0009070042,0.0005994047],"domain_scores_gemma":[0.9977174,0.0003052539,0.0002071938,0.0009507828,0.0004696624,0.0003497379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001952474,0.00003268335,6.711312e-7,0.00007310216,0.00001876603,0.00003236585,0.00006241471,0.02453982,0.000004551267,0.05344331,0.004889334,0.9169011],"study_design_scores_gemma":[0.00002038028,0.00006969544,0.000003176741,0.0005091449,0.00001683444,0.00001232202,0.00001850101,0.9773086,0.0006483011,0.01044805,0.01068037,0.0002645615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[6.136472e-7,0.01359927,0.9786362,0.004549692,0.0006609132,0.0008211643,0.000005902461,0.0005835581,0.001142702],"genre_scores_gemma":[0.0002475136,0.07459037,0.9217798,0.001633753,0.0002803523,0.00009820984,0.00004302117,0.00003746079,0.001289503],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9527689,"threshold_uncertainty_score":0.999999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09369246705703665,"score_gpt":0.3716525507103665,"score_spread":0.2779600836533298,"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."}}