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GSGP-C++ 2.0: A geometric semantic genetic programming framework
| Content Provider | Semantic Scholar |
|---|---|
| Author | Castelli, Mauro Manzoni, Luca |
| Copyright Year | 2019 |
| Abstract | 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. |
| Starting Page | 100313 |
| Ending Page | 100313 |
| Page Count | 1 |
| File Format | PDF HTM / HTML |
| DOI | 10.1016/j.softx.2019.100313 |
| Volume Number | 10 |
| Alternate Webpage(s) | https://run.unl.pt/bitstream/10362/81595/1/MCastelli_LManzoni_2019.pdf |
| Alternate Webpage(s) | https://doi.org/10.1016/j.softx.2019.100313 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
