{"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 International 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002234109,0.0001171483,0.0001413872,0.00005745685,0.0002133269,0.0001389589,0.000164714,0.00004767444,0.000003502699],"category_scores_gemma":[0.00004152351,0.00009834443,0.00005296126,0.00009545711,0.0000383597,0.000111033,0.00003029663,0.0001369579,1.172953e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007250474,"about_ca_system_score_gemma":0.0000308254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004598403,"about_ca_topic_score_gemma":0.000004509456,"domain_scores_codex":[0.9992214,0.00002582452,0.0003565674,0.0001025127,0.000144388,0.0001492391],"domain_scores_gemma":[0.9991459,0.0001819612,0.000143266,0.00004962177,0.0004276031,0.00005168125],"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":[0.00001175473,0.00001269715,0.00005667417,0.000006431109,0.0000908296,0.000001120456,0.00009752458,0.9814411,0.0001052086,0.00009514998,0.00001066625,0.01807089],"study_design_scores_gemma":[0.0008654933,0.00003328882,0.0001866565,0.00007726821,0.00004354061,0.0001091999,0.0001243994,0.9980716,0.00002082836,0.0002590257,0.0001001279,0.0001086022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01069047,0.0003332653,0.9880162,0.00004845553,0.0007445941,0.00009985996,0.00002152734,0.00003276941,0.00001283249],"genre_scores_gemma":[0.7040072,0.0000523996,0.2953946,0.00005957178,0.0004332349,9.271913e-7,0.00003642597,0.00001425069,0.000001426805],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6933167,"threshold_uncertainty_score":0.4010369,"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."}}