{"id":"W2147288701","doi":"10.1109/icma.2007.4304060","title":"A New Constrained Multiobjective Optimization Algorithm Based on Artificial Immune Systems","year":2007,"lang":"en","type":"article","venue":"","topic":"Artificial Immune Systems Applications","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematical optimization; Multi-objective optimization; Computer science; Constraint (computer-aided design); Ranking (information retrieval); Constrained optimization; Artificial immune system; Optimization problem; Test suite; Algorithm; Mathematics; Artificial intelligence; Test case; 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":[0.0008965109,0.0007359059,0.0008218239,0.0007615911,0.0003812027,0.0007079031,0.001295564,0.0009828348,0.002371559],"category_scores_gemma":[0.001711519,0.0003199162,0.0007363057,0.0006734905,0.0004074451,0.0009884236,0.001040806,0.0008134763,0.0004382423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004566662,"about_ca_system_score_gemma":0.001003284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001878661,"about_ca_topic_score_gemma":0.001695342,"domain_scores_codex":[0.999431,0.0001843408,0.00003526412,0.0000976197,0.0002059907,0.00004581028],"domain_scores_gemma":[0.9995155,0.0002257287,0.00005654963,0.00003714427,0.000136948,0.00002804214],"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":[0.00007329378,0.00008454297,0.0006870572,0.000112622,0.00009637282,0.00007695978,0.00006160083,0.7956573,0.006667896,0.01713011,0.002083882,0.1772684],"study_design_scores_gemma":[0.0000198191,0.00003475699,0.00007967404,0.000005867194,0.000007415202,0.00002543246,0.000005115422,0.9953502,0.0006844028,0.002359105,0.001421378,0.000006822635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006624794,0.000126227,0.9911302,0.00008250206,0.00003093273,0.00004993039,0.0000192487,0.0001334148,0.00180274],"genre_scores_gemma":[0.2093417,0.0002446109,0.7858586,0.000315595,0.00005608013,0.0004410314,0.0001689724,0.00008782398,0.003485651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002371559,"threshold_uncertainty_score":0.007933617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01025059508812272,"score_gpt":0.2306222283851549,"score_spread":0.2203716332970322,"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."}}