{"id":"W2117073511","doi":"10.2991/ijndc.2014.2.2.5","title":"Multi-Objective Optimization for Milling Operations using Genetic Algorithms under Various Constraints","year":2014,"lang":"en","type":"article","venue":"˜The œInternational journal of networked and distributed computing","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Collège de Maisonneuve; Concordia University","funders":"Natural Science Foundation of Hebei Province; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Genetic algorithm; Mathematical optimization; Algorithm; Machine learning; Mathematics","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":[0.000920339,0.001325919,0.001352014,0.000830172,0.0004780395,0.001135283,0.0009688067,0.001526642,0.001507289],"category_scores_gemma":[0.001700872,0.000601391,0.0009569753,0.00117532,0.0004968037,0.000876069,0.0006149972,0.001059828,0.000255612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009444845,"about_ca_system_score_gemma":0.001103607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005638515,"about_ca_topic_score_gemma":0.004248113,"domain_scores_codex":[0.9996402,0.000140663,0.0000135712,0.0000563594,0.0001088908,0.0000402824],"domain_scores_gemma":[0.9994235,0.0004083663,0.00006852794,0.00001979962,0.00006369295,0.00001607939],"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":[0.000007109836,0.00001031013,0.00007169184,0.00002456371,0.00001280498,0.00001617886,0.00001056131,0.9911646,0.0002806643,0.001958721,0.00009240687,0.006350352],"study_design_scores_gemma":[0.000004085102,0.00001098207,0.00002364139,0.000003720538,0.00000374387,0.000004069649,0.000004099973,0.9987336,0.0001140382,0.0009628198,0.0001334077,0.000001880447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02156943,0.0005073432,0.9743774,0.0001238939,0.00002538927,0.00005792417,0.00004831758,0.0001395899,0.003150735],"genre_scores_gemma":[0.6064049,0.0008826785,0.3881227,0.0001103149,0.00004366062,0.0004684349,0.0001857107,0.0001186227,0.003662958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005638515,"threshold_uncertainty_score":0.0112114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01528961434894643,"score_gpt":0.2600290921234231,"score_spread":0.2447394777744766,"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."}}