{"id":"W2061212936","doi":"10.1145/2725494.2725499","title":"Parallel Evolutionary Algorithms Performing Pairwise Comparisons","year":2015,"lang":"en","type":"preprint","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec","funders":"Center of Mathematical Sciences and Applications, Harvard University","keywords":"Differential evolution; Dimension (graph theory); Particle swarm optimization; Pairwise comparison; Logarithm; Population; Population size; Upper and lower bounds; Algorithm; Mathematical optimization; Evolutionary algorithm; Convergence (economics); Computer science; Evolutionary computation; Speedup; Mathematics; Simple (philosophy); Parallel computing; Combinatorics; Statistics; Mathematical analysis","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":[0.004845058,0.00177933,0.002062348,0.001442027,0.001256157,0.00186189,0.00327996,0.001672113,0.007387088],"category_scores_gemma":[0.02410853,0.0007592237,0.001127503,0.002842454,0.001217815,0.003269824,0.002497292,0.001851509,0.001859559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001245048,"about_ca_system_score_gemma":0.001670372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001740997,"about_ca_topic_score_gemma":0.002239097,"domain_scores_codex":[0.9961654,0.00123393,0.0001774192,0.0009199115,0.001245385,0.0002579616],"domain_scores_gemma":[0.9880861,0.006622491,0.0007423143,0.002739829,0.001564989,0.0002442907],"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":[0.0002644239,0.0002100849,0.001463695,0.0002746852,0.0001779679,0.0001190777,0.0002155266,0.6878235,0.008002453,0.06097724,0.002655717,0.2378156],"study_design_scores_gemma":[0.00006268659,0.00009639947,0.0003436283,0.00001149938,0.00002937047,0.00006617722,0.00003777514,0.9317643,0.005778835,0.05969953,0.00209359,0.00001631066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0353605,0.0002182179,0.954349,0.000183302,0.0001325081,0.0001575221,0.00009242565,0.001170331,0.008336265],"genre_scores_gemma":[0.2855175,0.000176727,0.7072579,0.0001164586,0.00007503388,0.0003957099,0.0002780037,0.0004020921,0.005780595],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007387088,"threshold_uncertainty_score":0.02562338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09041547469194562,"score_gpt":0.3325407560274917,"score_spread":0.2421252813355461,"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."}}