{"id":"W1596688073","doi":"","title":"An adaptive genetic algorithm for multi objective flexible manufacturing systems","year":2002,"lang":"en","type":"article","venue":"Genetic and Evolutionary Computation Conference","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Genetic algorithm; Computer science; Flexible manufacturing system; Field (mathematics); Multi-objective optimization; Distributed manufacturing; Pareto principle; Mathematical optimization; Computer-integrated manufacturing; Manufacturing engineering; Algorithm; Industrial engineering; Engineering; Mathematics; Machine learning; Scheduling (production processes)","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":[],"consensus_categories":[],"category_scores_codex":[0.0000297081,0.0001752862,0.0001486961,0.00008911217,0.0001844739,0.00005559671,0.00007398399,0.00007714063,0.00001567237],"category_scores_gemma":[0.000005526863,0.0001946699,0.00002315292,0.0000489064,0.00005759269,0.0001327323,0.00001440325,0.00007923568,0.00001013844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000652771,"about_ca_system_score_gemma":0.00001196324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001638672,"about_ca_topic_score_gemma":0.000001423331,"domain_scores_codex":[0.9991643,0.0000276211,0.0002092747,0.0002749077,0.000103356,0.000220482],"domain_scores_gemma":[0.999579,0.00005655743,0.00004757839,0.0001043026,0.0001164808,0.00009611073],"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":[0.000003746288,0.00001963897,0.00001773595,0.00002751713,0.00001690135,0.000001243293,0.0001662324,0.9040161,0.00001593487,0.0001197798,0.00009014126,0.09550502],"study_design_scores_gemma":[0.0004156912,0.0001423317,0.009427253,0.00001994867,0.00001890625,0.00002123439,0.0001783671,0.9877539,0.0001800194,0.001467591,0.0001364502,0.0002382638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006084315,0.001405961,0.9912655,0.000005839818,0.000326223,0.0003862571,0.00005459053,0.0002884228,0.0001828881],"genre_scores_gemma":[0.5893087,0.0001764652,0.4102567,0.00000677452,0.00007070201,0.00005162553,0.00003098659,0.00001653546,0.0000814128],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5832245,"threshold_uncertainty_score":0.7938407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0378006629596019,"score_gpt":0.2445967614484945,"score_spread":0.2067960984888926,"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."}}