Experiments with High Performance Genetic Programming for Classification Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Chitty:2016:SGAI,

• author = "Darren M. Chitty",
• title = "Experiments with High Performance Genetic Programming for Classification Problems",
• booktitle = "Proceedings of AI-2016, The Thirty-Sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence",
• year = "2016",
• editor = "Max Bramer and Miltos Petridis",
• pages = "221--227",
• address = "Cambridge",
• month = dec,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, GPU, Classification Parallel Processing",
• isbn13 = "978-3-319-47175-4",
• URL = "https://link.springer.com/chapter/10.1007/978-3-319-47175-4_15",
• DOI = "doi:10.1007/978-3-319-47175-4_15",
• abstract = "In recent years there have been many papers concerned with significantly improving the computational speed of Genetic Programming (GP) through exploitation of parallel hardware. The benefits of timeliness or being able to consider larger datasets are obvious. However, a question remains in whether there are wider benefits of this high performance GP approach. Consequently, this paper will investigate leveraging this performance by using a higher degree of evolution and ensemble approaches in order to discern if any improvement in classification accuracies can be achieved from high performance GP thereby advancing the technique itself.",

}

Genetic Programming entries for Darren M Chitty

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