{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000786397,0.001044,0.0009342105,0.001085725,0.0004356513,0.0009639215,0.001010888,0.00113298,0.001991071],"category_scores_gemma":[0.001625516,0.0005533459,0.001076291,0.0008085057,0.0005305996,0.0007213988,0.0007194507,0.0007053,0.0002609013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009376237,"about_ca_system_score_gemma":0.001426213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003937911,"about_ca_topic_score_gemma":0.003407382,"domain_scores_codex":[0.9996601,0.0001230032,0.00001560342,0.00006373379,0.00008587662,0.0000516481],"domain_scores_gemma":[0.9995511,0.0002644406,0.00006244898,0.00002773588,0.00006734388,0.00002691073],"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":[0.00001460806,0.00001975462,0.0002141525,0.00002063541,0.00001981065,0.0000309545,0.00001922214,0.9806075,0.0005997409,0.003209853,0.0002995092,0.01494431],"study_design_scores_gemma":[0.000007160706,0.00001445891,0.00005327171,0.000004581527,0.000007477337,0.000008429074,0.000007816665,0.9974017,0.000248562,0.001738168,0.0005052033,0.000003272993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04838172,0.0003511746,0.9434205,0.0002657404,0.00006221709,0.0001034799,0.00007351136,0.000283963,0.007057726],"genre_scores_gemma":[0.4192017,0.0003275568,0.5754734,0.0001481588,0.0000389557,0.0003486804,0.0002278459,0.0001172775,0.004116403],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003937911,"threshold_uncertainty_score":0.007829964,"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."}}