{"id":"W2005308601","doi":"10.1145/1570256.1570304","title":"Learning and using hyper-heuristics for variable and value ordering in constraint satisfaction problems","year":2009,"lang":"en","type":"article","venue":"","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Heuristics; Constraint satisfaction problem; Computer science; Heuristic; Hyper-heuristic; Constraint satisfaction; Local consistency; Artificial intelligence; Variable (mathematics); ENCODE; Theoretical computer science; Mathematical optimization; Machine learning; Mathematics","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":[0.003521979,0.0009443004,0.0009259136,0.001289263,0.0006273602,0.002243648,0.001540878,0.001076469,0.001622958],"category_scores_gemma":[0.01682752,0.0006723368,0.0009099775,0.001565364,0.002027806,0.00406398,0.001653476,0.001821866,0.0002013807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001769596,"about_ca_system_score_gemma":0.00228625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004698508,"about_ca_topic_score_gemma":0.008134406,"domain_scores_codex":[0.9973913,0.001596148,0.0001460099,0.000296716,0.0003989705,0.0001707283],"domain_scores_gemma":[0.9912574,0.006664498,0.0006031187,0.000737991,0.0005338914,0.000203121],"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":[0.0001576808,0.0001531687,0.002131909,0.0001766283,0.00009468634,0.00007722549,0.0003155247,0.8313612,0.001508727,0.05118717,0.0006310311,0.112205],"study_design_scores_gemma":[0.00003438861,0.00005625392,0.0001906728,0.00002754082,0.00001895513,0.00002018531,0.00007362731,0.9370118,0.0009974451,0.06096791,0.0005861762,0.00001505702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09155708,0.0003818645,0.9036554,0.0004878019,0.00002524439,0.0001527419,0.00009694602,0.0003978678,0.003245055],"genre_scores_gemma":[0.56551,0.000312079,0.4326155,0.0001802747,0.00002453755,0.0002364805,0.0001935513,0.00005818175,0.0008694718],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004698508,"threshold_uncertainty_score":0.01862621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01546574351656901,"score_gpt":0.2483395594489988,"score_spread":0.2328738159324298,"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."}}