{"id":"W1967756118","doi":"10.1007/s10957-004-0943-z","title":"Analysis of Random Restart and Iterated Improvement for Global Optimization with Application to the Traveling Salesman Problem","year":2005,"lang":"en","type":"article","venue":"Journal of Optimization Theory and Applications","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Acadia University","funders":"","keywords":"Travelling salesman problem; Mathematics; Iterated function; Theory of computation; A priori and a posteriori; Convergence (economics); Speedup; Mathematical optimization; Combinatorics; Rate of convergence; Applied mathematics; Algorithm; Computer science; Mathematical analysis","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":[],"consensus_categories":[],"category_scores_codex":[0.001698433,0.000115148,0.0002554863,0.0002326503,0.0002332371,0.0001548046,0.0003355767,0.00004211676,0.000007941871],"category_scores_gemma":[0.00009270223,0.00007949607,0.00005455924,0.001627397,0.00006872458,0.0003265472,0.00005375547,0.00007132391,2.645639e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004185099,"about_ca_system_score_gemma":0.00008232093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001295082,"about_ca_topic_score_gemma":0.00000384325,"domain_scores_codex":[0.9986264,0.0001374075,0.0005933196,0.0002349687,0.0002764617,0.0001314369],"domain_scores_gemma":[0.9978054,0.0003236221,0.0005176957,0.0002861524,0.0009641639,0.0001030334],"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":[0.000141302,0.0000662918,0.00006179482,0.00001285015,0.0001760507,6.627467e-8,0.0002341673,0.9171121,0.00004413338,0.05140924,0.00001450795,0.03072749],"study_design_scores_gemma":[0.0009267518,0.0001528523,0.0001089854,0.0000128868,0.0002911657,0.000009565962,0.00007935119,0.9964442,0.0001485031,0.001037653,0.0006995642,0.00008857049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004134253,0.0001187139,0.996365,0.001809556,0.00001010546,0.001161706,0.00001893908,0.00001501282,0.00008752316],"genre_scores_gemma":[0.1114881,0.0001922111,0.8876968,0.0002099817,0.00006798308,0.0002449771,0.00003348773,0.000009485937,0.00005701234],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1110747,"threshold_uncertainty_score":0.3241756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007913086695719185,"score_gpt":0.2766126644019262,"score_spread":0.2686995777062071,"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."}}