{"id":"W2064875201","doi":"10.1109/cec.2013.6557583","title":"Heterogeneous Multi-Population Cultural Algorithm","year":2013,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Benchmark (surveying); Local search (optimization); Population; Heuristic; Computer science; Convergence (economics); Set (abstract data type); Architecture; Mathematical optimization; Cultural algorithm; State (computer science); Space (punctuation); Algorithm; State space; Theoretical computer science; Optimization problem; Mathematics; Artificial intelligence; Geography; Statistics; Meta-optimization","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001314146,0.0001126775,0.0001148688,0.00009927369,0.0001104018,0.0004554627,0.0006128785,0.0000491376,0.0009237886],"category_scores_gemma":[0.00007424393,0.00008647137,0.00004702203,0.000338877,0.00002637322,0.0008312498,0.0002236569,0.00008869246,0.001631656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004046913,"about_ca_system_score_gemma":0.00001684707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004305386,"about_ca_topic_score_gemma":0.000004153204,"domain_scores_codex":[0.9987016,0.00008340301,0.0002256583,0.0003262481,0.0003753086,0.0002878064],"domain_scores_gemma":[0.9990329,0.00004125086,0.00004648587,0.0004374413,0.0002796657,0.0001622675],"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":[5.089149e-7,0.0001399761,0.0004856418,0.000008008657,0.00002803372,0.00001412811,0.0001977871,0.007059997,0.0001819287,0.003543533,0.004024267,0.9843162],"study_design_scores_gemma":[0.0002070372,0.00001934438,0.003196085,0.000001578267,9.672184e-7,0.00002245123,0.000007032011,0.9951915,0.0004292406,0.0003431054,0.0004583549,0.0001232659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006998554,0.00003100783,0.9970991,0.0004796354,0.0002254472,0.0002986547,8.84407e-7,0.0002679699,0.0008974203],"genre_scores_gemma":[0.04760304,0.00001175693,0.9468749,0.0002197461,0.0000482773,0.0000480764,0.00001209174,0.00000849097,0.005173668],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9881315,"threshold_uncertainty_score":0.9999895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03153938383097595,"score_gpt":0.3002613613897944,"score_spread":0.2687219775588185,"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."}}