{"id":"W2084993914","doi":"10.1016/j.camwa.2006.07.013","title":"A novel population initialization method for accelerating evolutionary algorithms","year":2007,"lang":"en","type":"article","venue":"Computers & Mathematics with Applications","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":377,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Population; Benchmark (surveying); Convergence (economics); Computer science; Mathematical optimization; Evolutionary algorithm; Algorithm; Set (abstract data type); Mathematics; 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":[],"consensus_categories":[],"category_scores_codex":[0.001178903,0.0002207434,0.0002648077,0.0003507083,0.0004812658,0.0002786123,0.0008200805,0.00009117396,0.000007590259],"category_scores_gemma":[0.0001167044,0.0002073636,0.0000677185,0.001222081,0.00004456049,0.0004475416,0.0001858556,0.0001355469,0.00001443881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001342998,"about_ca_system_score_gemma":0.0001120489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001547619,"about_ca_topic_score_gemma":0.000005456142,"domain_scores_codex":[0.9978204,0.00003668833,0.0006346247,0.0005451667,0.0005517905,0.0004113157],"domain_scores_gemma":[0.997163,0.00086399,0.0003489701,0.0007569473,0.0006857921,0.0001812862],"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":[0.000009813569,0.0005541407,0.00005828265,0.000190828,0.00007280452,0.00000168446,0.0008195598,0.0235802,0.0006605432,0.8041804,0.000476123,0.1693956],"study_design_scores_gemma":[0.0005820245,0.00006009531,0.0006186399,0.00003509347,0.00002014716,0.00006398024,0.00004662304,0.9832091,0.0001856465,0.01346678,0.001466468,0.0002453827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0000278703,0.00002400655,0.9965509,0.0003410873,0.0001051639,0.001980591,0.00001572343,0.0003311338,0.0006234802],"genre_scores_gemma":[0.002213321,0.000004153945,0.9965015,0.0001669251,0.0001940591,0.0006303668,0.0001572153,0.00003675677,0.00009571484],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9596289,"threshold_uncertainty_score":0.8456042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0573682288893775,"score_gpt":0.3569625786743005,"score_spread":0.2995943497849231,"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."}}