 |
Conference papers
A new adaptive sampling approach for Genetic Programming
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. In this paper, 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 halfway 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.
Cited literature [19 references]
https://hal-univ-paris10.archives-ouvertes.fr/hal-02476544
Contributor : Sana Ben Hamida <>
Submitted on : Wednesday, February 12, 2020 - 5:21:30 PM
Last modification on : Wednesday, October 28, 2020 - 9:52:06 AM
Long-term archiving on: : Wednesday, May 13, 2020 - 6:45:21 PM
Contributor : Sana Ben Hamida <>
Submitted on : Wednesday, February 12, 2020 - 5:21:30 PM
Last modification on : Wednesday, October 28, 2020 - 9:52:06 AM
Long-term archiving on: : Wednesday, May 13, 2020 - 6:45:21 PM
File
adaptiveSampling for GP.pdf
Files produced by the author(s)