{"id":"W2932517410","doi":"10.1007/978-981-13-6569-0_10","title":"Expectation Algorithm (ExA): A Socio-inspired Optimization Methodology","year":2019,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"CMA-ES; Backtracking; Algorithm; Mathematical optimization; Computer science; Wilcoxon signed-rank test; Particle swarm optimization; Optimization problem; Mathematics; Artificial intelligence; Evolution strategy; Evolutionary algorithm; Statistics","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":[0.001385046,0.0005760153,0.0009511991,0.001023925,0.0002291021,0.0001363996,0.0014086,0.0003780144,0.0003167908],"category_scores_gemma":[0.0009637937,0.0006104733,0.0001982543,0.000456423,0.0005403131,0.000437101,0.0009324601,0.0007005694,0.000491308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000644718,"about_ca_system_score_gemma":0.000637426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001128115,"about_ca_topic_score_gemma":0.000005358173,"domain_scores_codex":[0.9952456,0.0003775026,0.001282618,0.001319287,0.001284294,0.0004907205],"domain_scores_gemma":[0.9929923,0.003824892,0.0006312068,0.0006843777,0.001757895,0.0001093605],"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":[0.000006473162,0.00002587608,0.00000348339,0.00006229508,0.0001520408,0.0000386014,0.001070843,0.6785297,2.108153e-7,0.2560194,0.0004736215,0.06361736],"study_design_scores_gemma":[0.0001623609,0.0001132986,0.000006983161,0.0001717929,0.00001768993,0.00002856273,0.0001710322,0.8324446,0.00001315602,0.1653918,0.001007451,0.0004712358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[3.764809e-7,0.002578295,0.9716374,0.0007863521,0.001820851,0.0008171565,0.00002903323,0.0001714992,0.022159],"genre_scores_gemma":[0.0001201539,0.002031179,0.9537257,0.0002858638,0.0001898401,0.00007099867,0.0001435155,0.00006323397,0.04336954],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1539149,"threshold_uncertainty_score":0.9996347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2318761286488891,"score_gpt":0.4198666105355522,"score_spread":0.1879904818866631,"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."}}