{"id":"W2108591779","doi":"10.3138/infor.47.1.23","title":"Multi-Attribute Portfolio Selection with Genetic Optimization Algorithms","year":2009,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Selection (genetic algorithm); Computer science; Portfolio; Quality control and genetic algorithms; Portfolio optimization; Genetic algorithm; Algorithm; Mathematical optimization; Machine learning; Artificial intelligence; Meta-optimization; Mathematics; Economics; Financial economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002834653,0.001179271,0.001569415,0.002610955,0.000489157,0.001424182,0.001500611,0.001744773,0.004848383],"category_scores_gemma":[0.00537423,0.0007746809,0.001195549,0.00279466,0.0004671895,0.001236917,0.001146278,0.001494573,0.001002949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001117401,"about_ca_system_score_gemma":0.001105304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00333705,"about_ca_topic_score_gemma":0.002779124,"domain_scores_codex":[0.9987366,0.0006658328,0.00005823022,0.0002036439,0.0002590463,0.00007673397],"domain_scores_gemma":[0.9983643,0.001184783,0.00009781263,0.0001073639,0.0001900164,0.00005564507],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001157128,0.0001415951,0.0006942826,0.00006598022,0.000164577,0.00005071221,0.00003755866,0.8038877,0.0005311515,0.007452871,0.002620786,0.1842371],"study_design_scores_gemma":[0.00001853331,0.00002274108,0.00008618122,0.000007324069,0.00001131363,0.00001091971,0.000003879725,0.9958727,0.0001837076,0.003393482,0.0003845255,0.000004831321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01846661,0.0005574534,0.9749882,0.0003288389,0.00006949442,0.0001411431,0.0001292019,0.0006078793,0.004711118],"genre_scores_gemma":[0.3111662,0.0003430304,0.6811714,0.0002158616,0.0001067286,0.0004947667,0.000352629,0.0001364697,0.006012966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004848383,"threshold_uncertainty_score":0.0162195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1120760793167675,"score_gpt":0.4081918378083836,"score_spread":0.2961157584916161,"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."}}