{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009260559,0.0005395242,0.001036761,0.001284048,0.0006408919,0.000107416,0.001790375,0.0005920254,0.00001013548],"category_scores_gemma":[0.000168495,0.0006005245,0.0003366562,0.0009847663,0.0002253864,0.00097687,0.0008160237,0.0007479556,0.000001450724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006993439,"about_ca_system_score_gemma":0.001068564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00184036,"about_ca_topic_score_gemma":0.00005817813,"domain_scores_codex":[0.9964024,0.0006483052,0.0003533257,0.001369196,0.0007667287,0.0004600618],"domain_scores_gemma":[0.995661,0.0009737384,0.0005639521,0.001297588,0.001143632,0.0003600657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01193437,0.04656127,0.3772513,0.01071157,0.0867909,0.06246178,0.142342,0.009230508,0.002701298,0.006452824,0.1201722,0.1233899],"study_design_scores_gemma":[0.009234197,0.01630269,0.01628285,0.0008959224,0.02118527,0.001283052,0.0207,0.03546623,0.00556196,0.0001018257,0.8678005,0.005185477],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2453554,0.0008450035,0.724652,0.00008733654,0.002100796,0.003191518,0.0001921906,0.002109403,0.02146637],"genre_scores_gemma":[0.758822,0.0006676108,0.1920959,0.00003080038,0.0005735792,0.000006520472,0.00007937075,0.0001660058,0.04755823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7476283,"threshold_uncertainty_score":0.9996446,"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."}}