{"id":"W1969837585","doi":"10.1109/nabic.2009.5393704","title":"Solving multiple-objective optimization problems using GISMOO algorithm","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Benchmark (surveying); Pareto principle; Computer science; Genetic algorithm; Algorithm; Artificial immune system; Multi-objective optimization; Mathematical optimization; Optimization algorithm; Optimization problem; Pareto optimal; Artificial intelligence; Mathematics; Machine learning","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":[0.0007558312,0.0009371391,0.001031712,0.001106394,0.0004816525,0.0005706362,0.0009887174,0.001077572,0.001842062],"category_scores_gemma":[0.0009083248,0.0002901927,0.0008674944,0.001019796,0.0003732745,0.0008011628,0.0008495285,0.0007052002,0.0004361452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003889698,"about_ca_system_score_gemma":0.0007114192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001422469,"about_ca_topic_score_gemma":0.00211961,"domain_scores_codex":[0.9996371,0.00009212882,0.00001882688,0.00005223922,0.0001671716,0.00003248201],"domain_scores_gemma":[0.9997963,0.00009457262,0.00002779046,0.00001711282,0.00004873337,0.0000154409],"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":[0.00005497777,0.00008736029,0.001114702,0.0001703178,0.0001190425,0.0001294747,0.00005407661,0.799065,0.004443277,0.0137878,0.001524712,0.1794492],"study_design_scores_gemma":[0.0000223074,0.00005008286,0.0002066325,0.00001054027,0.00001256005,0.00004137402,0.00001323144,0.9923023,0.0008926942,0.004073258,0.002367505,0.000007520075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01496522,0.0004005947,0.9805787,0.0001085404,0.00005038083,0.00005283043,0.00003250747,0.0003357061,0.003475526],"genre_scores_gemma":[0.2310748,0.0005065212,0.7646993,0.0001571804,0.00005919334,0.0002535592,0.0002489292,0.0001132945,0.002887226],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001842062,"threshold_uncertainty_score":0.006162286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01842879423602076,"score_gpt":0.260995659927023,"score_spread":0.2425668656910022,"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."}}