{"id":"W2729858878","doi":"10.1109/cec.2017.7969592","title":"Fusion-based hybrid many-objective optimization algorithm","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Benchmark (surveying); Algorithm; Optimization problem; Convergence (economics); Test functions for optimization; Curse of dimensionality; Mathematical optimization; Machine learning; 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":[0.001822332,0.001192269,0.001729243,0.001255123,0.0006601325,0.001202048,0.002000884,0.001652189,0.002266884],"category_scores_gemma":[0.001721288,0.0005622449,0.001372164,0.001332206,0.0007637769,0.001128099,0.001827345,0.001238691,0.0005572219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008241357,"about_ca_system_score_gemma":0.00138026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00473956,"about_ca_topic_score_gemma":0.003044328,"domain_scores_codex":[0.9990528,0.0002410608,0.0000537215,0.0001819097,0.0003585211,0.0001120462],"domain_scores_gemma":[0.9993932,0.0002775305,0.00006809505,0.00004305096,0.0001881388,0.00002996673],"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":[0.00006600298,0.00004298842,0.0004005836,0.00006753708,0.00007188366,0.00002963338,0.00004974418,0.9329261,0.001579584,0.004105199,0.0006102276,0.06005059],"study_design_scores_gemma":[0.00001076415,0.00002705488,0.00006150117,0.000005440795,0.000008336282,0.000008456898,0.000004832982,0.9982798,0.0003003876,0.0009497463,0.0003396299,0.000004032824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01088992,0.0005396885,0.9844229,0.0001163046,0.00004874993,0.00005812643,0.0000327627,0.0005090471,0.00338259],"genre_scores_gemma":[0.5571242,0.0004778229,0.4371972,0.0002901611,0.00007094981,0.0004807258,0.0002877828,0.0001426507,0.00392842],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00473956,"threshold_uncertainty_score":0.009637535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01255830147542072,"score_gpt":0.2671091091938244,"score_spread":0.2545508077184037,"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."}}