GSGP-C++ 2.0: A geometric semantic genetic programming framework

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@Article{CASTELLI:2019:SoftwareX,

• author = "Mauro Castelli and Luca Manzoni",
• title = "{GSGP-C++} 2.0: A geometric semantic genetic programming framework",
• journal = "SoftwareX",
• volume = "10",
• pages = "100313",
• year = "2019",
• ISSN = "2352-7110",
• DOI = "doi:10.1016/j.softx.2019.100313",
• URL = "http://www.sciencedirect.com/science/article/pii/S2352711019301736",
• keywords = "genetic algorithms, genetic programming, Semantics, Machine learning",
• 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",

}

Genetic Programming entries for Mauro Castelli Luca Manzoni

Citations