{"id":"W2808165282","doi":"10.1109/icdcs.2018.00031","title":"PEA: Parallel Evolutionary Algorithm by Separating Convergence and Diversity for Large-Scale Multi-Objective Optimization","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Evolutionary algorithm; Computer science; Convergence (economics); Selection (genetic algorithm); Process (computing); Scale (ratio); Operator (biology); Evolutionary computation; Speedup; Mathematical optimization; Parallel algorithm; Algorithm; Machine learning; Mathematics; Parallel computing","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":[0.0002387693,0.0002359643,0.0002154938,0.00009881394,0.001242959,0.00007785809,0.000421595,0.0001078676,0.0000499766],"category_scores_gemma":[0.0001025116,0.0002431564,0.00005848464,0.0004090179,0.0001577132,0.001434843,0.0008591493,0.0001025472,0.00001727616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000134822,"about_ca_system_score_gemma":0.00005905258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004006061,"about_ca_topic_score_gemma":0.00001980387,"domain_scores_codex":[0.9981999,0.00007571251,0.0002538904,0.0007936424,0.0002514186,0.0004254699],"domain_scores_gemma":[0.9984661,0.0001384348,0.0001679891,0.0003129555,0.0007546271,0.0001598656],"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":[0.0003859724,0.003565655,0.03212132,0.0001585924,0.0006682426,0.00002714599,0.04330251,0.7716855,0.00132786,0.02030669,0.04450287,0.08194762],"study_design_scores_gemma":[0.001632558,0.0001638713,0.001084021,0.000007632613,0.00000991619,0.00001003848,0.0003664585,0.9952641,0.0003814189,0.000278945,0.0004807506,0.000320362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001173905,0.0001436236,0.9978221,0.0001404572,0.0003830905,0.0006866378,0.00008308225,0.0002875344,0.0003360662],"genre_scores_gemma":[0.008491113,0.00005056325,0.9895385,0.0003152964,0.00008961004,0.00005862037,0.00004365934,0.00001713469,0.001395495],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2235785,"threshold_uncertainty_score":0.9915633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01431556691641537,"score_gpt":0.2690055937748692,"score_spread":0.2546900268584538,"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."}}