{"id":"W1580329799","doi":"10.1109/cec.2015.7256970","title":"Leaders and followers — A new metaheuristic to avoid the bias of accumulated information","year":2015,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Metaheuristic; Particle swarm optimization; Differential evolution; Modal; Computer science; Mathematical optimization; Identification (biology); Random search; Range (aeronautics); Local search (optimization); Artificial intelligence; Algorithm; Mathematics; Engineering","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":[0.001197128,0.0001184404,0.000181376,0.0002636467,0.00006483298,0.0002475787,0.0007396553,0.00004521197,0.00005222098],"category_scores_gemma":[0.002831054,0.00007761057,0.00003701523,0.0009796876,0.00007658042,0.0007498312,0.0003283354,0.000112117,0.0002534775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000294405,"about_ca_system_score_gemma":0.0002926013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003603776,"about_ca_topic_score_gemma":0.00002456526,"domain_scores_codex":[0.9983683,0.0001478705,0.0003863082,0.0001794568,0.0006833229,0.0002347166],"domain_scores_gemma":[0.9982557,0.0003151357,0.0001058241,0.0005815552,0.0003838159,0.0003579887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002046945,0.0001974734,0.001385219,0.0001789076,0.0004508694,0.000006300847,0.05811785,0.1545868,0.0004892052,0.08104853,0.403242,0.3000921],"study_design_scores_gemma":[0.001832551,0.0002666905,0.001024104,0.00002941324,0.00003723727,0.00002804614,0.001100811,0.8527728,0.0007292721,0.003129741,0.1386348,0.0004146044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002716768,0.00005434483,0.9883124,0.005080114,0.0001940745,0.0003698858,0.000001828217,0.00007732707,0.003193315],"genre_scores_gemma":[0.1134664,0.00002540811,0.878428,0.001716411,0.00004102508,0.00001664356,0.00001057413,0.00001295909,0.006282591],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6981859,"threshold_uncertainty_score":0.3389241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2187635084740322,"score_gpt":0.3525116640843118,"score_spread":0.1337481556102797,"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."}}