{"id":"W1994895518","doi":"10.1007/s10898-007-9234-1","title":"Nonsmooth optimization through Mesh Adaptive Direct Search and Variable Neighborhood Search","year":2007,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":185,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Air Force Office of Scientific Research","keywords":"Mathematics; Variable neighborhood search; Mathematical optimization; Metaheuristic; Convergence (economics); Local search (optimization); Variable (mathematics); Guided Local Search; Algorithm","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":[0.00328646,0.0002663189,0.0004570952,0.0002856858,0.000283322,0.000488432,0.0008627698,0.0001868971,0.0001395937],"category_scores_gemma":[0.0005651054,0.0002417758,0.000096293,0.002536157,0.0001329949,0.002229713,0.000353935,0.0003984001,0.000007668465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004574342,"about_ca_system_score_gemma":0.0006523264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000564047,"about_ca_topic_score_gemma":0.000002877318,"domain_scores_codex":[0.9960027,0.0004029568,0.0009508406,0.0004527295,0.001563759,0.0006269968],"domain_scores_gemma":[0.9962236,0.0003355058,0.0003701444,0.0004216572,0.002260257,0.000388872],"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":[0.0001184625,0.0001523266,0.0004820714,0.0000154017,0.00007907621,0.00004850242,0.0002121949,0.9687564,0.000009066946,0.02451492,0.0002632015,0.005348428],"study_design_scores_gemma":[0.001158692,0.0004556973,0.0004860624,0.00005698383,0.00003199924,0.0002329694,0.0001215003,0.9965358,0.0001357055,0.0004399988,0.0001162137,0.0002284162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001243343,0.000423672,0.9842018,0.0005950191,0.0004288334,0.0003350979,0.00001233884,0.00006215821,0.01381675],"genre_scores_gemma":[0.05963765,0.0006130883,0.9392055,0.0001792776,0.0001937348,0.000002022733,0.000008224722,0.00002274761,0.0001377971],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05951332,"threshold_uncertainty_score":0.985933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02169005376408899,"score_gpt":0.2979204776592476,"score_spread":0.2762304238951586,"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."}}