{"id":"W2132721978","doi":"10.1145/2330163.2330174","title":"Gaussian mixture modeling for dynamic particle swarm optimization of recurrent problems","year":2012,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Particle swarm optimization; Optimization problem; Embedding; Multi-swarm optimization; Mathematical optimization; Mixture model; Focus (optics); Digital watermarking; Representation (politics); Gaussian; Algorithm; Artificial intelligence; Image (mathematics); Mathematics","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":[0.0006553094,0.0001015632,0.0001476414,0.00008805885,0.00007667084,0.0000636883,0.0004169561,0.00007304058,0.00005435691],"category_scores_gemma":[0.0001622043,0.00008498535,0.0000536614,0.0004233833,0.0000209523,0.0005413308,0.0001246534,0.00009927547,0.000009274045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003953277,"about_ca_system_score_gemma":0.00005354733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006202545,"about_ca_topic_score_gemma":0.000001693263,"domain_scores_codex":[0.9986715,0.000064196,0.0003339002,0.000224864,0.0003275768,0.0003779695],"domain_scores_gemma":[0.9990233,0.00007283637,0.00008766261,0.0003780665,0.0002780064,0.0001601681],"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":[0.000004928143,0.0001377088,0.00004499112,0.00004862298,0.00001063681,6.898584e-8,0.0003468179,0.9762495,0.00009853364,0.01430549,0.00009319011,0.008659558],"study_design_scores_gemma":[0.0002884055,0.00005739002,0.00001029052,0.00001401491,0.000005919504,0.000001900981,0.00002033437,0.998086,0.0008970118,0.0003996759,0.0001161431,0.0001028764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005338168,0.0002413763,0.9972385,0.0007059096,0.0002492932,0.0005080522,0.000003945872,0.00008279995,0.0004362793],"genre_scores_gemma":[0.3394662,0.00003587205,0.6600696,0.00003127928,0.00002516002,0.00005333261,0.000009949857,0.00000981129,0.0002987273],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3389324,"threshold_uncertainty_score":0.3465602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04198200099278054,"score_gpt":0.3134523187551221,"score_spread":0.2714703177623415,"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."}}