{"id":"W4249797588","doi":"10.1007/978-1-4419-1665-5_2","title":"Tabu Search","year":2010,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Tabu search; Guided Local Search; Hill climbing; Diversification (marketing strategy); Computer science; Heuristic; Space (punctuation); Incremental heuristic search; Beam search; Theoretical computer science; Search algorithm; Algorithm; Artificial intelligence","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":["metaresearch","metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["sts","scholarly_communication","open_science","insufficient_payload"],"category_scores_codex":[0.03601986,0.0009854385,0.0007624888,0.02656626,0.005308941,0.0132894,0.02693638,0.0004326284,0.004916768],"category_scores_gemma":[0.002718696,0.001037111,0.0002219732,0.01129899,0.01506273,0.01497769,0.01944273,0.005031215,0.001821536],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006262109,"about_ca_system_score_gemma":0.002617373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009157369,"about_ca_topic_score_gemma":0.005021536,"domain_scores_codex":[0.9656008,0.0006730749,0.002410395,0.004810544,0.02309264,0.003412509],"domain_scores_gemma":[0.9858925,0.000437659,0.0001420566,0.004121123,0.008497735,0.000908952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009230632,0.0004188885,0.00008241829,0.00007348658,0.0001092804,0.0006366787,0.0007993515,0.06974421,0.000813488,0.9181811,0.001024786,0.008024017],"study_design_scores_gemma":[0.002374313,0.000439438,0.002892254,0.001043174,0.00001892409,0.0001687818,0.002973392,0.5250876,0.001712123,0.05854915,0.4024623,0.002278524],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0006850372,0.0001167199,0.02923548,0.02906314,0.005434699,0.005581704,0.0001598267,0.0002761875,0.9294472],"genre_scores_gemma":[0.03512755,0.008685044,0.2013899,0.0002766222,0.0006069287,0.002074723,0.0003397725,0.0001547656,0.7513446],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.859632,"threshold_uncertainty_score":0.9992079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06679086680121286,"score_gpt":0.4115067670879551,"score_spread":0.3447159002867423,"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."}}