{"id":"W2166674691","doi":"10.1109/tsmcc.2007.900651","title":"Optimal Advertising Campaign Generation for Multiple Brands Using MOGA","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Greedy algorithm; Mathematical optimization; Heuristic; Genetic algorithm; Computer science; Variety (cybernetics); Encoding (memory); Pareto optimal; Set (abstract data type); Pareto principle; Optimization problem; Multi-objective optimization; Key (lock); Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000505291,0.0002120521,0.0002937767,0.0001442912,0.0005902874,0.0001594559,0.0001464556,0.00009705738,0.000002454445],"category_scores_gemma":[0.000006449807,0.0001958598,0.00007566232,0.0003129853,0.00007127068,0.0002520043,0.000004506417,0.000112001,0.00000538085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006212857,"about_ca_system_score_gemma":0.0000241001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001690717,"about_ca_topic_score_gemma":0.00002230185,"domain_scores_codex":[0.9985071,0.00006065236,0.0005311278,0.000510823,0.0001391135,0.0002511707],"domain_scores_gemma":[0.9989737,0.0001276948,0.0001945883,0.0003871888,0.0001577509,0.0001591248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003829784,0.0004906736,0.0000776511,0.0004680369,0.0001138623,0.000002618398,0.001388377,0.3776732,0.01297607,0.007624625,0.000247323,0.5988993],"study_design_scores_gemma":[0.0007367106,0.00007457729,0.00002891356,0.0001075237,0.000050448,0.00005545057,0.00008412928,0.9379947,0.002387729,0.00002480764,0.05815005,0.0003049426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001482001,0.002970489,0.9931973,0.00003574781,0.000316763,0.001821968,0.00002320259,0.00007622816,0.0000763151],"genre_scores_gemma":[0.4895667,0.005768564,0.5022597,0.0001679957,0.0003044168,0.0008972312,0.00001693968,0.00004617251,0.0009722551],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5985944,"threshold_uncertainty_score":0.7986931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03964088752379955,"score_gpt":0.2961882225148139,"score_spread":0.2565473349910143,"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."}}