{"id":"W2911022377","doi":"10.47749/t/unicamp.2017.986608","title":"Meta-heurísticas GRASP e BRKGA aplicadas ao problema da diversidade máxima","year":2017,"lang":"gl","type":"dissertation","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Air (Canada); Adidas (Canada)","funders":"","keywords":"GRASP; Metaheuristic; Mathematical optimization; Path (computing); Sorting; Heuristics; Mathematics; Benchmark (surveying); Local search (optimization); Set (abstract data type); Function (biology); Fitness landscape; Greedy randomized adaptive search procedure; Computer science; Algorithm; Greedy algorithm; Population","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":[0.001277288,0.00144595,0.001333277,0.001716611,0.0005336928,0.001461465,0.001303938,0.0016761,0.00225085],"category_scores_gemma":[0.00316206,0.0004752296,0.001642655,0.001635623,0.0007570725,0.00115726,0.0008958037,0.001110238,0.0004790702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001079364,"about_ca_system_score_gemma":0.001441933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002076218,"about_ca_topic_score_gemma":0.002154288,"domain_scores_codex":[0.9991946,0.0003286957,0.00003599231,0.00009938381,0.0002339141,0.0001073946],"domain_scores_gemma":[0.9989364,0.0006498147,0.0001687388,0.00008954201,0.0001161925,0.00003936215],"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":[0.0000715096,0.00007999474,0.0005431947,0.0002574646,0.0001102939,0.00007792333,0.00005643991,0.9216108,0.001685434,0.01469601,0.0008136979,0.0599972],"study_design_scores_gemma":[0.00002421403,0.0000878911,0.0001878956,0.00004101518,0.00003850382,0.00008021059,0.00004451612,0.9886663,0.001277073,0.007215254,0.002325639,0.00001153324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04823077,0.002868751,0.9375988,0.0004236935,0.0001206995,0.0001804926,0.0001392276,0.0005895493,0.009848004],"genre_scores_gemma":[0.5204632,0.002561417,0.4711939,0.0002079355,0.0001142992,0.0005000311,0.0002362493,0.0001831597,0.004539832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00225085,"threshold_uncertainty_score":0.007831395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1750849405682838,"score_gpt":0.3702698097201531,"score_spread":0.1951848691518693,"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."}}