{"id":"W1989986531","doi":"10.1109/smc.2014.6974290","title":"On VEPSO and VEDE for solving a treaty optimization problem","year":2014,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Mathematical optimization; Optimization problem; Vector optimization; Differential evolution; Particle swarm optimization; Computer science; Multi-swarm optimization; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002183289,0.0009869477,0.001088358,0.0009435743,0.0004355712,0.0009618436,0.0009629077,0.001534942,0.002158315],"category_scores_gemma":[0.006755655,0.0004038229,0.000626946,0.0009518284,0.0007467975,0.0008002677,0.001249498,0.001270569,0.0002718447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006941877,"about_ca_system_score_gemma":0.0009780858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005838909,"about_ca_topic_score_gemma":0.004541412,"domain_scores_codex":[0.9994246,0.0002712624,0.00003442646,0.00004707046,0.0001761009,0.00004657845],"domain_scores_gemma":[0.9975451,0.001893522,0.00009914294,0.00008883473,0.0003115222,0.00006185845],"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":[0.00009957534,0.00007362854,0.0007322265,0.0001116322,0.00005014618,0.00004501763,0.00003952129,0.9331244,0.0006336343,0.009157212,0.0007575224,0.05517549],"study_design_scores_gemma":[0.00001610268,0.00006242372,0.0001204996,0.00001324586,0.000007137914,0.00001400459,0.000009724991,0.9969929,0.0002787233,0.001789632,0.0006908503,0.000004751063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08337502,0.001886475,0.8967571,0.0009001118,0.0002815031,0.0002298072,0.0001066695,0.0003818298,0.01608159],"genre_scores_gemma":[0.475318,0.001117594,0.5179283,0.0005038674,0.0001134272,0.0004641614,0.0002963056,0.0001579019,0.004100331],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005838909,"threshold_uncertainty_score":0.01160985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0167563250541879,"score_gpt":0.2705751100408024,"score_spread":0.2538187849866145,"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."}}