{"id":"W2886040690","doi":"10.1016/j.amc.2018.07.037","title":"Estimation distribution algorithms on constrained optimization problems","year":2018,"lang":"en","type":"article","venue":"Applied Mathematics and Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"EDAS; Estimation of distribution algorithm; Mathematical optimization; Mathematics; Selection (genetic algorithm); Benchmark (surveying); Evolutionary algorithm; Optimization problem; Algorithm; Gaussian; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005076731,0.0001602002,0.0001711575,0.0001191161,0.000290527,0.0003195093,0.0002082912,0.00007135017,0.00001844934],"category_scores_gemma":[0.00008827882,0.0001505006,0.00002149624,0.0004649763,0.0001237883,0.0001973749,0.0001064964,0.00009197921,0.0000641757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004515463,"about_ca_system_score_gemma":0.00004552644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000198582,"about_ca_topic_score_gemma":2.839668e-7,"domain_scores_codex":[0.9985943,0.0000240342,0.0003543499,0.0003623414,0.0004300063,0.0002349382],"domain_scores_gemma":[0.9990533,0.0001776413,0.0001876005,0.0002453403,0.0002385843,0.00009754312],"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":[0.00000475488,0.000156099,6.217331e-7,0.00006224866,0.00001617975,8.972008e-7,0.0006274814,0.3353348,0.000102571,0.3930779,0.0002369976,0.2703794],"study_design_scores_gemma":[0.0004225441,0.000141703,0.00001879874,0.00002684193,0.000007467285,0.00001008216,0.00003200784,0.9691494,0.0005355267,0.02944908,0.00005451868,0.0001520207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005119845,0.000006359955,0.9948341,0.0002919073,0.0001048325,0.000522679,0.000006870949,0.0001969788,0.003524266],"genre_scores_gemma":[0.1841349,0.00001066164,0.8155286,0.00007305043,0.00005139459,0.00004717516,0.0001164231,0.000012204,0.00002563598],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6338146,"threshold_uncertainty_score":0.6137238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02523527753296024,"score_gpt":0.2853783659093228,"score_spread":0.2601430883763626,"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."}}