{"id":"W4412026837","doi":"10.1016/j.neucom.2025.130917","title":"A reinforcement learning-assisted genetic programming algorithm for team formation problem considering person-job matching","year":2025,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Matching (statistics); Genetic algorithm; Artificial intelligence; Genetic programming; Reinforcement; Mathematical optimization; Algorithm; Machine learning; Mathematics; Psychology","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":[0.0005510678,0.0003152051,0.0003064041,0.0003272378,0.0009170314,0.0007767195,0.0007174591,0.00009488881,0.000001839917],"category_scores_gemma":[0.0001256166,0.0003421312,0.0001545993,0.0006026534,0.0000358962,0.0005415105,0.0004741526,0.0004589445,0.00001069493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000177551,"about_ca_system_score_gemma":0.0001235933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002138499,"about_ca_topic_score_gemma":0.00000138669,"domain_scores_codex":[0.997442,0.0001150626,0.0006892395,0.0005986302,0.0003996968,0.0007553568],"domain_scores_gemma":[0.9985097,0.0003761996,0.0004248867,0.0003995052,0.0001956668,0.00009402452],"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":[0.000002321668,0.00001016949,0.0001878086,0.0001723795,0.00002278672,0.000004392881,0.001129623,0.6442512,0.0004173767,0.001121252,0.0001082691,0.3525724],"study_design_scores_gemma":[0.0007797812,0.0001972465,0.0004920117,0.0002992639,0.00002357317,0.00004639805,0.0002728433,0.9891511,0.0005126254,0.0001350183,0.007793131,0.0002970055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002826575,0.00004163475,0.9916428,0.0002973565,0.0004240068,0.001431662,1.514216e-7,0.0008242857,0.002511502],"genre_scores_gemma":[0.2957373,0.000003668574,0.7029628,0.0003490179,0.00008472581,0.0001101027,0.000006561258,0.00002742342,0.0007184236],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3522754,"threshold_uncertainty_score":0.9999031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02050964793677034,"score_gpt":0.2583166601061052,"score_spread":0.2378070121693348,"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."}}