{"id":"W2274259390","doi":"","title":"Center-based initialization for large-scale black-box problems","year":2009,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; Ontario Tech University","funders":"","keywords":"Initialization; Population; Computer science; Mathematical optimization; Benchmark (surveying); Latin hypercube sampling; Differential evolution; Particle swarm optimization; Algorithm; Monte Carlo method; Mathematics; Statistics","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":[0.0006085897,0.0002250404,0.00020173,0.0003801279,0.0001752434,0.0006734375,0.001553237,0.0001006095,0.0004397509],"category_scores_gemma":[0.0004584453,0.0002262533,0.0001110894,0.0004544793,0.00007643276,0.0004442206,0.00008707345,0.0001976652,0.000317511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00010932,"about_ca_system_score_gemma":0.0002107044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006084541,"about_ca_topic_score_gemma":0.00002194021,"domain_scores_codex":[0.9973545,0.0001055103,0.0006185113,0.0006616646,0.0008425651,0.00041723],"domain_scores_gemma":[0.9977185,0.0001670285,0.0001967662,0.0004847509,0.001269374,0.0001636264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006172807,0.0006105954,0.00001773965,0.000009482887,0.00001218695,0.000004722439,0.0004088564,0.02061627,0.0007193859,0.9010997,0.0002702839,0.07616899],"study_design_scores_gemma":[0.0001274611,0.0003035397,0.00002971083,0.00005821129,0.000002632506,0.000001881527,0.00004989821,0.8652788,0.01502721,0.1168616,0.002049377,0.000209643],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002293839,0.000004670576,0.9790244,0.009318966,0.0008388709,0.0006024409,0.00006723712,0.0001800285,0.00973398],"genre_scores_gemma":[0.8838123,0.00002443154,0.1136888,0.001596984,0.0002179863,0.00007525568,0.0001360369,0.00001544623,0.0004327431],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8835829,"threshold_uncertainty_score":0.9226343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1344271892125133,"score_gpt":0.3802287657734609,"score_spread":0.2458015765609476,"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."}}