Pietropolli, GloriaManzoni, LucaPaoletti, AlessiaCastelli, Mauro2023-11-062023-11-062023-121389-2576PURE: 72363135PURE UUID: 6383b255-30cf-4597-b621-c19471123afeScopus: 85175168117WOS: 001088866300001ORCID: /0000-0002-8793-1451/work/151444375http://hdl.handle.net/10362/159617Pietropolli, G., Manzoni, L., Paoletti, A., & Castelli, M. (2023). On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers. Genetic Programming And Evolvable Machines, 24(2 Special Issue on Highlights of Genetic Programming 2022 Events), 1-20. [16]. https://doi.org/10.21203/rs.3.rs-2229748/v1, https://doi.org/10.1007/s10710-023-09463-1---Open access funding provided by Università degli Studi di Trieste within the CRUI-CARE Agreement. This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the Project—UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMSGeometric semantic genetic programming (GSGP) is a popular form of GP where the effect of crossover and mutation can be expressed as geometric operations on a semantic space. A recent study showed that GSGP can be hybridized with a standard gradient-based optimized, Adam, commonly used in training artificial neural networks.We expand upon that work by considering more gradient-based optimizers, a deeper investigation of their parameters, how the hybridization is performed, and a more comprehensive set of benchmark problems. With the correct choice of hyperparameters, this hybridization improves the performances of GSGP and allows it to reach the same fitness values with fewer fitness evaluations.202472160engAdamEvolutionary algorithmGeometric semantic genetic programmingStochastic gradient descentSoftwareTheoretical Computer ScienceHardware and ArchitectureComputer Science Applicationsjournal article10.21203/rs.3.rs-2229748/v1https://www.scopus.com/pages/publications/85175168117https://github.com/gpietrop/GradientBasedGSGPhttps://www.webofscience.com/wos/woscc/full-record/WOS:001088866300001