{"id":"W2325049811","doi":"10.2514/6.2014-3144","title":"On Using Genetic Algorithm Optimized Activation Functions to Increase Neural Network Accuracy","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Sensor Technologies Research","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science; Artificial neural network; Genetic algorithm; Algorithm; Artificial intelligence; 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":[0.001932337,0.0008940827,0.0006105748,0.0008455153,0.0003378499,0.0008259802,0.0007607152,0.001066114,0.000703564],"category_scores_gemma":[0.006290069,0.000331852,0.0004067243,0.0006675886,0.0006126162,0.0008151426,0.0004909166,0.0008655594,0.0002263539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009725046,"about_ca_system_score_gemma":0.001082318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007159599,"about_ca_topic_score_gemma":0.005855632,"domain_scores_codex":[0.9994439,0.0001810216,0.00003125461,0.00008167762,0.0001990154,0.00006311081],"domain_scores_gemma":[0.9983924,0.000967894,0.0001289244,0.0001053744,0.0003859237,0.00001943493],"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":[0.00004211058,0.00003322832,0.0005638034,0.00001922437,0.00001886976,0.00002964203,0.00003187787,0.9456468,0.003313106,0.002069054,0.0002507883,0.04798143],"study_design_scores_gemma":[0.000004491724,0.00001843274,0.0000992011,0.000005444282,0.00000400444,0.000006936612,0.000003345566,0.9976733,0.001471821,0.0005301911,0.0001800983,0.000002757942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1560517,0.0008299879,0.8331059,0.0005366827,0.00008994852,0.0001020328,0.0000373876,0.001008814,0.008237657],"genre_scores_gemma":[0.6698123,0.0004008874,0.3267943,0.0002451289,0.00003511438,0.0001302743,0.00007843827,0.0001405624,0.002363082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007159599,"threshold_uncertainty_score":0.01423585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02301762267903271,"score_gpt":0.2808723401686417,"score_spread":0.257854717489609,"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."}}