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Publicação
GSGP-C++ 2.0
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Orientador(es)
Resumo(s)
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.
Descrição
Castelli, M., & Manzoni, L. (2019). GSGP-C++ 2.0: A geometric semantic genetic programming framework. SoftwareX, 10, [100313]. https://doi.org/10.1016/j.softx.2019.100313
Palavras-chave
Genetic programming Machine learning Semantics Software Computer Science Applications