{"id":"W2016472674","doi":"10.1239/jap/1261670682","title":"Geometric Convergence of Genetic Algorithms Under Tempered Random Restart","year":2009,"lang":"en","type":"article","venue":"Journal of Applied Probability","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crossover; Mathematics; Algorithm; Convergence (economics); Population; Genetic algorithm; Rate of convergence; Mathematical optimization; Mutation; Class (philosophy); Computer science; Key (lock); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002799516,0.0001718179,0.0005503677,0.0004695489,0.00007814661,0.000081677,0.001312123,0.00009404038,0.0001211865],"category_scores_gemma":[0.0005318639,0.0001390053,0.0001615135,0.001872205,0.0001533464,0.0002472721,0.0001298952,0.0003477207,0.0000126038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001181865,"about_ca_system_score_gemma":0.0004455647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003715264,"about_ca_topic_score_gemma":3.944851e-7,"domain_scores_codex":[0.9966648,0.0001850121,0.001254981,0.0003414543,0.001226974,0.0003267373],"domain_scores_gemma":[0.9969588,0.0004068344,0.0007188958,0.0007384082,0.000968145,0.0002089506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002373025,0.004734866,0.004302421,0.0005247821,0.0005105755,0.0001414646,0.002019241,0.2983475,0.01306553,0.06950227,0.004474099,0.6000043],"study_design_scores_gemma":[0.01245557,0.002298784,0.1193804,0.00009787108,0.000113625,0.0002538298,0.0001238517,0.3538386,0.03079313,0.4784156,0.001265471,0.0009633327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05367514,0.0002382448,0.9438027,0.0006160874,0.000246466,0.0004582884,0.000002471784,0.00002478637,0.0009358648],"genre_scores_gemma":[0.539588,0.0000952554,0.4601309,0.00008956841,0.00005841276,0.000003397989,4.13766e-7,0.000005416682,0.00002865162],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5990409,"threshold_uncertainty_score":0.5668471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02888184872187093,"score_gpt":0.2774924382518765,"score_spread":0.2486105895300055,"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."}}