{"id":"W1833684988","doi":"10.1007/0-387-23152-8_57","title":"A New Method to Construct the Non-Dominated Set in Multi-Objective Genetic Algorithms","year":2006,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Construct (python library); Benchmark (surveying); Convergence (economics); Set (abstract data type); Mathematical optimization; Computer science; Algorithm; Genetic algorithm; Cluster analysis; Multi-objective optimization; Mathematics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004235033,0.0008659257,0.0008595957,0.000754116,0.0001751951,0.0002192751,0.001840395,0.0004466644,0.0001832879],"category_scores_gemma":[0.00006218042,0.0006927836,0.0002314562,0.0006035914,0.0001368359,0.0003221682,0.0008836233,0.0008195471,0.000292789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005512917,"about_ca_system_score_gemma":0.0005795322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001099637,"about_ca_topic_score_gemma":0.0005178005,"domain_scores_codex":[0.9960521,0.0001524924,0.0008578885,0.001646963,0.0006189654,0.0006715402],"domain_scores_gemma":[0.9969475,0.0005015768,0.0004173119,0.001348155,0.0004839319,0.0003015262],"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":[0.000052409,0.0001062871,0.00004284962,0.0000299554,0.0002842417,0.0004897114,0.002844073,0.1999216,0.000173275,0.03965797,0.007693795,0.7487039],"study_design_scores_gemma":[0.003147548,0.0001968399,0.00103084,0.0001632576,0.00005328982,0.0002491112,0.00007282155,0.9597177,0.0008597093,0.01227166,0.02048429,0.001752909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[5.867567e-7,0.0001288464,0.9057192,0.0002602139,0.0004973688,0.001925841,0.0000456327,0.0002102113,0.09121211],"genre_scores_gemma":[0.00003298021,0.00001838729,0.7563311,0.0005655976,0.0001075394,0.00006313961,0.00001743253,0.00008755578,0.2427763],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7597961,"threshold_uncertainty_score":0.9995523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02084364257414391,"score_gpt":0.302866864529187,"score_spread":0.2820232219550431,"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."}}