{"id":"W3029701887","doi":"10.1145/3387168.3387205","title":"Solving Dynamic Multi-Objective Optimization Problems Using Cultural Algorithm based on Decomposition","year":2019,"lang":"en","type":"article","venue":"Proceedings of the 3rd International Conference on Vision, Image and Signal Processing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Benchmark (surveying); Mathematical optimization; Decomposition; Multi-objective optimization; Optimization problem; Computer science; Population; Algorithm; Local optimum; Mathematics; Chemistry","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":[0.001539594,0.001422494,0.001372929,0.0009330991,0.0006622561,0.001272683,0.001028695,0.001272208,0.001256234],"category_scores_gemma":[0.00293799,0.0004600105,0.001304663,0.0008373058,0.0008356296,0.0009224464,0.001615487,0.001560901,0.0002152587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007607732,"about_ca_system_score_gemma":0.001742562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005402989,"about_ca_topic_score_gemma":0.003744549,"domain_scores_codex":[0.9993353,0.0002991047,0.00004161219,0.00009723158,0.0001496771,0.00007709607],"domain_scores_gemma":[0.9989319,0.0006528387,0.00009896543,0.00006828982,0.0001738558,0.00007415067],"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":[0.00002701002,0.00004147704,0.0006180388,0.00004883644,0.00005829108,0.00004070596,0.00005150718,0.9612539,0.001220191,0.005729235,0.0004471876,0.03046365],"study_design_scores_gemma":[0.00001067202,0.00003210135,0.00006330691,0.000007189197,0.000007179064,0.00001264421,0.00001341507,0.9972037,0.0002597252,0.002044882,0.0003410668,0.000004122172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02936607,0.0003812647,0.9658858,0.0001668393,0.0000529705,0.00008299064,0.00003362338,0.0001647286,0.003865706],"genre_scores_gemma":[0.4478387,0.000462916,0.5488373,0.0001986025,0.00004873965,0.0004679468,0.0002040923,0.00007708714,0.00186462],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005402989,"threshold_uncertainty_score":0.01074308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0175769250040428,"score_gpt":0.3094245261946589,"score_spread":0.2918476011906161,"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."}}