{"id":"W2153654605","doi":"10.1016/j.dam.2005.05.020","title":"First vs. best improvement: An empirical study","year":2005,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":115,"is_retracted":false,"has_abstract":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Heuristics; Constructive; Travelling salesman problem; Mathematics; Enhanced Data Rates for GSM Evolution; Heuristic; Mathematical optimization; Greedy algorithm; Algorithm; Combinatorics; Computer science; Artificial intelligence; Process (computing)","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":[0.01761632,0.0008676432,0.001969558,0.004463752,0.001327844,0.002048174,0.00224899,0.001920997,0.005765733],"category_scores_gemma":[0.1054603,0.0004904683,0.001801585,0.003129821,0.001010893,0.004367893,0.001226549,0.003541416,0.001507354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001680406,"about_ca_system_score_gemma":0.001479418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002216036,"about_ca_topic_score_gemma":0.003118022,"domain_scores_codex":[0.9898432,0.003713263,0.0007181701,0.001424391,0.003389417,0.0009115223],"domain_scores_gemma":[0.6548538,0.294845,0.01121482,0.023351,0.01104132,0.004694001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01399885,0.006157978,0.2621965,0.002184174,0.001681499,0.0004693818,0.001514112,0.1098241,0.00425588,0.01037535,0.02041923,0.5669231],"study_design_scores_gemma":[0.001569916,0.0236206,0.3929496,0.0006710176,0.00351511,0.005431843,0.003045578,0.4978823,0.02111485,0.02811505,0.02173116,0.0003530141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9581467,0.008086776,0.01447681,0.0008260066,0.0001592618,0.0002177962,0.00213207,0.001086635,0.01486806],"genre_scores_gemma":[0.9815547,0.0007355266,0.01347094,0.00009824863,0.00007219275,0.00006051844,0.001152574,0.0003241666,0.002531114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01761632,"threshold_uncertainty_score":0.0931651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03618088057861051,"score_gpt":0.3343979954373477,"score_spread":0.2982171148587372,"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."}}