Kim, Min HyeokMcKay, Robert IanHoai, Nguyen XuanKim, Kangil2026-01-012026-01-0197836422040670302-9743https://hdl.handle.net/1885/733798858We investigate the application of adaptive operator selection rates to Genetic Programming. Results confirm those from other areas of evolutionary algorithms: adaptive rate selection out-performs non-adaptive methods, and among adaptive methods, adaptive pursuit out-performs probability matching. Adaptive pursuit combined with a reward policy that rewards the overall fitness change in the elite worked best of the strategies tested, though not uniformly on all problems.Acknowledgments. Seoul National University Institute for Computer Technology provided research facilities for this study, which was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Project No. 2010-0012546), and the BK21-IT program of MEST.12enAdaptive Operator SelectionAdaptive PursuitEvolutionary AlgorithmGenetic ProgrammingGrammar Guided Genetic ProgrammingProbability MatchingTree Adjoining Grammar201110.1007/978-3-642-20407-4_1979955764587