{"id":"W4313416446","doi":"10.20998/2079-0775.2022.2.01","title":"REVIEW OF MODERN USE OF GENETIC AND EVOLUTIONARY ALGORITHMS. STRATEGIES, POSSIBILITIES (REVIEW ARTICLE)","year":2022,"lang":"en","type":"article","venue":"Bulletin of the National Technical University «KhPI» Series Engineering and CAD","topic":"Engineering Technology and Methodologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hatch (Canada)","funders":"","keywords":"Computer science; Relevance (law); Randomness; Management science; Adaptation (eye); Artificial intelligence; Evolutionary algorithm; Data science; Machine learning; Mathematics; Engineering","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":[0.0002648862,0.0001045466,0.0002485016,0.00007154181,0.00005056451,0.000001887314,0.0001976435,0.00006246177,0.0000278758],"category_scores_gemma":[0.0003138754,0.0001024187,0.00006519784,0.0001919291,0.0001717841,0.00004635202,0.0002380582,0.000218739,9.657889e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005163157,"about_ca_system_score_gemma":0.00002844819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001278209,"about_ca_topic_score_gemma":4.630263e-7,"domain_scores_codex":[0.9993461,0.00004381679,0.0002207495,0.0001119233,0.0001780565,0.00009929993],"domain_scores_gemma":[0.9995193,0.0001726863,0.00006041558,0.0001422287,0.00008226905,0.00002310473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000115346,0.0001979985,0.0004348551,0.06409537,0.0004798112,0.00001368939,0.000184416,0.7751561,0.03099783,0.08454927,0.03222673,0.01154852],"study_design_scores_gemma":[0.001953208,0.001004762,0.08919841,0.02673385,0.0009835071,0.001066595,0.0007239642,0.05426823,0.009087701,0.01494876,0.7979035,0.002127459],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.2205331,0.7473277,0.02235252,0.005155606,0.000366387,0.001341677,0.0008491459,0.00116856,0.0009052074],"genre_scores_gemma":[0.635131,0.2604406,0.1036825,0.0001725242,0.00002070537,0.00001865758,0.00001445643,0.00003829544,0.000481223],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.7656768,"threshold_uncertainty_score":0.4176514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0233959527229744,"score_gpt":0.2192699176347676,"score_spread":0.1958739649117932,"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."}}