{"id":"W2165687335","doi":"10.1109/tevc.2009.2017517","title":"Bi-Objective Multipopulation Genetic Algorithm for Multimodal Function Optimization","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Evolutionary Computation","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":114,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Cluster analysis; Local optimum; Mathematical optimization; Genetic algorithm; Population; Artificial intelligence; Algorithm; Mathematics; Machine learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003007636,0.0003173267,0.0002601577,0.0008012868,0.0007529976,0.0001739395,0.0003589837,0.0001899801,0.00003671249],"category_scores_gemma":[0.00003469536,0.0003565601,0.0001862452,0.001201451,0.00005487545,0.001117292,0.000003616757,0.0002541374,0.00005599011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004205288,"about_ca_system_score_gemma":0.0001846765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002294,"about_ca_topic_score_gemma":0.00000176159,"domain_scores_codex":[0.9971387,0.0002475391,0.0006133942,0.0008247101,0.0007453168,0.0004303324],"domain_scores_gemma":[0.997973,0.0003250108,0.0002277981,0.0003996492,0.000897592,0.0001769416],"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":[0.00004465249,0.0002784164,0.000001634261,0.000005406472,0.00002390328,9.969774e-7,0.00007381372,0.6607782,0.0000779425,0.0001697357,0.0001303944,0.3384148],"study_design_scores_gemma":[0.001612531,0.000823964,0.00364171,0.00002079795,0.00003415993,0.00001791721,0.00001900193,0.9901311,0.0003938779,0.002899856,0.00005640693,0.0003486522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001562995,0.0000482723,0.9957219,0.0004834584,0.001326234,0.001572251,0.0000507296,0.000553821,0.00008706431],"genre_scores_gemma":[0.2481944,0.00002031711,0.7509962,0.0001872723,0.0001295019,0.0001711999,0.0001004161,0.00002391786,0.000176786],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3380662,"threshold_uncertainty_score":0.9998887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01815384982913662,"score_gpt":0.2780752823084465,"score_spread":0.2599214324793099,"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."}}