{"id":"W2898863354","doi":"10.1007/978-3-319-99719-3_4","title":"A General Method for Selection Function Optimization in Genetic Algorithms","year":2018,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Selection (genetic algorithm); Mathematical optimization; Population; Computer science; Fitness function; Set (abstract data type); Genetic algorithm; Mutation; Optimization problem; Algorithm; Meta-optimization; Artificial intelligence; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001641172,0.0005550132,0.0007381849,0.001215588,0.0001281795,0.0004522318,0.0008291927,0.0004652444,0.0002466003],"category_scores_gemma":[0.0007522575,0.0006171968,0.00009006613,0.0003825585,0.00008755876,0.0003082381,0.0003169919,0.0005748738,0.00004872296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004849294,"about_ca_system_score_gemma":0.0002656579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001488393,"about_ca_topic_score_gemma":0.00002324633,"domain_scores_codex":[0.9960709,0.000024966,0.001320368,0.001038229,0.0009173531,0.0006282186],"domain_scores_gemma":[0.9970611,0.0003636229,0.000732023,0.000353164,0.001342875,0.0001472534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004106318,0.0002416158,0.00002983512,0.001869323,0.0001231886,0.00001982421,0.001916111,0.0398443,0.0000487047,0.901203,0.003744157,0.0509189],"study_design_scores_gemma":[0.0004842968,0.0001820511,0.00001990097,0.0001945652,0.00004348662,0.00001665407,0.00001284148,0.7586625,0.00003231598,0.2361418,0.003771593,0.0004380198],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000003279569,0.00007256035,0.9850658,0.00004518068,0.000440396,0.001678088,0.00006067068,0.0001365431,0.01249752],"genre_scores_gemma":[0.000002698265,0.0002085651,0.9477664,0.00004715229,0.0003350172,0.0002371598,0.0000418903,0.0001421155,0.05121901],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7188182,"threshold_uncertainty_score":0.9996279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0308797905523363,"score_gpt":0.308801472387631,"score_spread":0.2779216818352947,"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."}}