A new adaptive sampling approach for Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8592

@InProceedings{Hmida:2019:ICDS,

• author = "Hmida Hmida and Sana Ben Hamida and Amel Borgi and Marta Rukoz",
• booktitle = "2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS)",
• title = "A new adaptive sampling approach for Genetic Programming",
• year = "2019",
• editor = "Jaouad Boumhidi and Hani Hagras and {El Habib} Nfaoui",
• month = "28-30 " # oct,
• address = "Marrakech, Morocco",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.researchnetwork.ma/icds2019/doc/ICDS2019ProgramV2.pdf",
• DOI = "doi:10.1109/ICDS47004.2019.8942353",
• abstract = "Genetic Programming (GP) is afflicted by an excessive computation time that is more exacerbated with data intensive problems. This issue has been addressed with different approaches such as sampling techniques or distributed implementations. we focus on dynamic sampling algorithms that mostly give to GP learner a new sample each generation. In so doing, individuals do not have enough time to extract the hidden knowledge. We propose adaptive sampling which is half-way between static and dynamic methods. It is a flexible approach applicable to any dynamic sampling. We implemented some variants based on controlling re-sampling frequency that we experimented to solve KDD intrusion detection problem with GP. The experimental study demonstrates how it preserves the power of dynamic sampling with possible improvements in learning time and quality for some sampling algorithms. This work opens many new relevant extension paths.",
• notes = "Also known as \cite{8942353}",

}

Genetic Programming entries for Hmida Hmida Sana Ben Hamida Amel Borgi Marta Rukoz

Citations