Castelli, MauroManzoni, Luca2019-09-172019-09-172019-07-01PURE: 14716677PURE UUID: a23c13ca-dcaf-4ecc-9ceb-71755d0d3829Scopus: 85071718915WOS: 000504065000020ORCID: /0000-0002-8793-1451/work/72856174http://www.scopus.com/inward/record.url?scp=85071718915&partnerID=8YFLogxKCastelli, M., & Manzoni, L. (2019). GSGP-C++ 2.0: A geometric semantic genetic programming framework. SoftwareX, 10, [100313]. https://doi.org/10.1016/j.softx.2019.100313Geometric semantic operators (GSOs) for Genetic Programming have been widely investigated in recent years, producing competitive results with respect to standard syntax based operator as well as other well-known machine learning techniques. The usage of GSOs has been facilitated by a C++ framework that implements these operators in a very efficient manner. This work presents a description of the system and focuses on a recently implemented feature that allows the user to store the information related to the best individual and to evaluate new data in a time that is linear with respect to the number of generations used to find the optimal individual. The paper presents the main features of the system and provides a step by step guide for interested users or developers.4530646engGenetic programmingMachine learningSemanticsSoftwareComputer Science Applicationsjournal article10.1016/j.softx.2019.100313https://www.scopus.com/pages/publications/85071718915https://www.webofscience.com/wos/woscc/full-record/WOS:000504065000020https://doi.org/10.24433/CO.5881521.v1https://github.com/ElsevierSoftwareX/SOFTX%5F2019_170