{"id":"W2108679901","doi":"10.1109/foci.2007.372151","title":"Opposition-Based Differential Evolution (ODE) with Variable Jumping Rate","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Jumping; Ode; Benchmark (surveying); Differential evolution; Suite; Opposition (politics); Computer science; Test suite; Mathematics; Mathematical optimization; Applied mathematics; Statistics; Test case; Geology; Geodesy; Law","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":[0.000254259,0.0001005995,0.00007703006,0.00009094026,0.0003106692,0.00008620905,0.00030527,0.00004218722,0.00006699195],"category_scores_gemma":[0.000004033109,0.00008243269,0.00002789181,0.0005024398,0.0000365479,0.0003407854,0.00004818179,0.00008319863,0.00004554242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001274549,"about_ca_system_score_gemma":0.0001153592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009361529,"about_ca_topic_score_gemma":0.00001924508,"domain_scores_codex":[0.9990822,0.00002349257,0.0001579601,0.0002952461,0.0001795095,0.0002616072],"domain_scores_gemma":[0.9993294,0.00009462765,0.00005336705,0.0003353991,0.00009140887,0.00009576369],"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":[0.000008637665,0.0001421551,0.0004177627,0.000005505188,0.00001004688,0.000002987737,0.00001898789,0.002310981,0.008818819,0.9872079,0.0004597899,0.0005963644],"study_design_scores_gemma":[0.0007120192,0.0001121206,0.02395313,0.00002754711,0.00001282576,0.00001513214,0.00002364735,0.9470319,0.004791748,0.0217395,0.001281134,0.0002993275],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005350898,0.000009268123,0.9876834,0.0007040741,0.00009649884,0.0001588928,0.000002076418,0.0002455899,0.005749354],"genre_scores_gemma":[0.7518529,3.587237e-7,0.247465,0.0002088853,0.00007759422,0.00001987385,0.00001255193,0.000005156982,0.0003576623],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9654685,"threshold_uncertainty_score":0.3361508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008114385425783906,"score_gpt":0.2217740595669562,"score_spread":0.2136596741411723,"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."}}