{"id":"W2599286397","doi":"10.1007/s10710-017-9298-8","title":"A univariate marginal distribution algorithm based on extreme elitism and its application to the robotic inverse displacement problem","year":2017,"lang":"en","type":"article","venue":"Genetic Programming and Evolvable Machines","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"British Columbia Knowledge Development Fund; Canada Foundation for Innovation","keywords":"Particle swarm optimization; Computer science; Algorithm; Univariate; Differential evolution; Heuristic; Displacement (psychology); Domain (mathematical analysis); Mathematical optimization; Mathematics; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00206835,0.000673617,0.00120409,0.0009543406,0.000728378,0.0009048902,0.001894605,0.001355546,0.002758055],"category_scores_gemma":[0.005591786,0.0004266935,0.000884456,0.001261973,0.001638919,0.001383747,0.001925409,0.001827481,0.0003628435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001120605,"about_ca_system_score_gemma":0.001470852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002667289,"about_ca_topic_score_gemma":0.002551032,"domain_scores_codex":[0.9993333,0.0002751007,0.00002283162,0.00007864184,0.0002202597,0.00006979205],"domain_scores_gemma":[0.998051,0.001353879,0.0001191044,0.0001041294,0.0002782516,0.00009351299],"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":[0.00008008191,0.000100965,0.000660193,0.00004649909,0.00004711573,0.00005861304,0.00008490501,0.8605723,0.001666805,0.04323788,0.001200586,0.09224398],"study_design_scores_gemma":[0.000005582539,0.00001647572,0.00005949345,0.000002344511,0.000003535612,0.00001336651,0.000005228256,0.9942114,0.0002505819,0.005185074,0.0002415008,0.00000537909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007046633,0.00008409284,0.9913759,0.0001135744,0.00002495689,0.00001711692,0.000007526249,0.0001552586,0.001174931],"genre_scores_gemma":[0.3823455,0.0003062951,0.610779,0.000188963,0.0000950703,0.0002356843,0.0000851001,0.0002762635,0.005688133],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002758055,"threshold_uncertainty_score":0.01093864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01966617289943487,"score_gpt":0.2691151408858998,"score_spread":0.2494489679864649,"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."}}