{"id":"W4247450063","doi":"10.32920/ryerson.14647074","title":"RankGPES: learning to rank for information retrieval using a hybrid genetic programming with evolutionary strategies","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Rank (graph theory); Genetic programming; Computer science; Ranking (information retrieval); Learning to rank; Artificial intelligence; Evolutionary algorithm; Machine learning; Evolutionary programming; Genetic algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001512737,0.0008111008,0.001185007,0.001121631,0.0004564225,0.001126914,0.001364476,0.001624718,0.002298385],"category_scores_gemma":[0.003279281,0.0003736741,0.0007930955,0.0009216281,0.0006012021,0.001176556,0.0009351448,0.001383231,0.000587597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006815958,"about_ca_system_score_gemma":0.0012234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003459969,"about_ca_topic_score_gemma":0.003620459,"domain_scores_codex":[0.999157,0.0003212924,0.00003679799,0.0001245944,0.0002815813,0.00007877251],"domain_scores_gemma":[0.9988895,0.0006997063,0.0000723456,0.00006720489,0.0002259993,0.0000451846],"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":[0.0001034347,0.0002330188,0.001193551,0.0001228741,0.0001014701,0.00008760364,0.0000776665,0.6688545,0.003080864,0.01751143,0.004330198,0.3043033],"study_design_scores_gemma":[0.00001665893,0.00006314609,0.00007963352,0.000005146577,0.000009108447,0.00001902701,0.000007977936,0.9956244,0.0006709769,0.002709642,0.0007877654,0.000006656692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02200759,0.0003626111,0.9727916,0.0003735964,0.00008179328,0.0001567021,0.00007524903,0.0009017487,0.00324915],"genre_scores_gemma":[0.2763277,0.0003082208,0.7154492,0.0004749527,0.0001141058,0.0004130156,0.0002782443,0.0001832418,0.006451307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003459969,"threshold_uncertainty_score":0.008000195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02839525941810875,"score_gpt":0.2993532365904498,"score_spread":0.270957977172341,"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."}}