GSGP-C++ 2.0

Abstract

Geometric 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.