{"id":"W2963239290","doi":"10.1007/s10479-019-03343-7","title":"Biologically Inspired Parent Selection in Genetic Algorithms","year":2019,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Quality control and genetic algorithms; Genetic algorithm; Selection (genetic algorithm); Computer science; Genetic representation; Theory of computation; Algorithm; Mathematical optimization; Simple (philosophy); Artificial intelligence; Meta-optimization; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002436928,0.0001022244,0.0002033051,0.0008853577,0.0001567977,0.000211691,0.0009899966,0.00009274718,0.0003355608],"category_scores_gemma":[0.0007738998,0.00008931255,0.0000469511,0.00252056,0.0001137611,0.0003591631,0.0003595421,0.0003633408,0.0003228685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004089408,"about_ca_system_score_gemma":0.000385864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004916345,"about_ca_topic_score_gemma":0.00007148031,"domain_scores_codex":[0.9968411,0.0007483513,0.0004793722,0.0004773327,0.000952702,0.000501175],"domain_scores_gemma":[0.9975671,0.0002529101,0.00002749397,0.0004627531,0.001563497,0.0001262545],"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":[0.00007945683,0.002085334,0.04233538,0.00013225,0.0001003378,0.00004591493,0.001406606,0.6540633,0.02274877,0.09053703,0.003773995,0.1826916],"study_design_scores_gemma":[0.0002727835,0.0003687812,0.05936906,0.00001955564,3.86344e-7,0.000004478197,0.00003205179,0.934696,0.003367926,0.0005029615,0.001256583,0.0001094117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.551259,0.0004670838,0.4324397,0.008042311,0.0001935628,0.001888445,0.00001552128,0.00009966661,0.005594656],"genre_scores_gemma":[0.8628719,0.0006273912,0.1346989,0.0001156816,0.00004415042,0.0001253763,0.00001036496,0.00001122656,0.001494916],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3116129,"threshold_uncertainty_score":0.4149929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2337803283620561,"score_gpt":0.4511889642799065,"score_spread":0.2174086359178504,"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."}}