{"id":"W2952040607","doi":"","title":"An Empirical Study of Meta- and Hyper-Heuristic Search for Multi-Objective Release Planning","year":2018,"lang":"en","type":"report","venue":"Utrecht University Repository (Utrecht University)","topic":"Software Engineering Techniques and Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Heuristics; Heuristic; Computer science; Meta heuristic; Variety (cybernetics); Genetic algorithm; Machine learning; Hyper-heuristic; Beam search; Artificial intelligence; Empirical research; Incremental heuristic search; Quality (philosophy); Search algorithm; Data mining; Mathematical optimization; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01205502,0.0006076808,0.000546868,0.001941249,0.0004929227,0.001135668,0.001365391,0.0009713789,0.001400552],"category_scores_gemma":[0.06104394,0.0003712419,0.000594101,0.003649795,0.0008556108,0.002420824,0.000678667,0.001328127,0.0002049119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001315648,"about_ca_system_score_gemma":0.001037624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003229264,"about_ca_topic_score_gemma":0.004887766,"domain_scores_codex":[0.9918799,0.006002343,0.0003964311,0.0005076799,0.001039171,0.000174387],"domain_scores_gemma":[0.8677359,0.1199284,0.003653069,0.004823298,0.003277269,0.0005819997],"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":[0.001457239,0.002454435,0.07287955,0.001467964,0.0007783462,0.0001925993,0.0005165741,0.7059069,0.001385479,0.008090638,0.004235887,0.2006343],"study_design_scores_gemma":[0.0003611426,0.002289171,0.04489931,0.0002808674,0.0001928932,0.0003430887,0.0007424224,0.9387419,0.002487689,0.004244201,0.005358357,0.00005898309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9520193,0.004316427,0.03418692,0.0006550296,0.0000458747,0.0002987543,0.0009157981,0.0002343966,0.007327416],"genre_scores_gemma":[0.9574425,0.0007442136,0.03988193,0.00007973635,0.00001780381,0.000217106,0.001067121,0.00005029398,0.0004991816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01205502,"threshold_uncertainty_score":0.06375384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1264703427016955,"score_gpt":0.3400049486874419,"score_spread":0.2135346059857464,"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."}}