{"id":"W2793816938","doi":"10.1007/s10462-018-9616-4","title":"A comprehensive investigation into the performance, robustness, scalability and convergence of chaos-enhanced evolutionary algorithms with boundary constraints","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Chaotic; Robustness (evolution); Computer science; Scalability; Evolutionary algorithm; Algorithm; Mathematical optimization; Convergence (economics); Heuristic; Mathematics; Machine learning; Artificial intelligence","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001075376,0.0002006524,0.0003749653,0.00008727092,0.0004005836,0.0000847022,0.0007506917,0.00005375491,0.0001860061],"category_scores_gemma":[0.0003480866,0.0001361997,0.00004735226,0.00138952,0.003886251,0.0005320346,0.0002762767,0.0002068391,0.00006311241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004793389,"about_ca_system_score_gemma":0.0004422847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005658482,"about_ca_topic_score_gemma":0.00001472693,"domain_scores_codex":[0.9974871,0.0003582314,0.0007101443,0.0005341565,0.000614844,0.0002954946],"domain_scores_gemma":[0.9970481,0.0003172682,0.0002824339,0.0007113337,0.001496062,0.0001448204],"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":[0.00002170759,0.00008082291,0.0003644859,0.001591608,0.00004605219,0.000002767637,0.001531186,0.0006583838,0.0004669335,0.01431622,0.0001242697,0.9807956],"study_design_scores_gemma":[0.00005835778,0.0004790918,0.001540612,0.001790319,0.00003853389,0.00005006099,0.0002402446,0.9688447,0.02096696,0.00452463,0.001125614,0.0003408737],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02899287,0.01020241,0.9575495,0.001805446,0.0002380839,0.00101216,0.000003714466,0.00005693185,0.0001389506],"genre_scores_gemma":[0.7590774,0.03472487,0.2051983,0.0006783443,0.0001226148,0.0001287608,0.00001068619,0.0000175554,0.00004154555],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9804547,"threshold_uncertainty_score":0.9988246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05226224469424341,"score_gpt":0.3155321147267129,"score_spread":0.2632698700324695,"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."}}