Pedram, Mahdi and Mousavirad, Seyed Jalaleddin and Schaefer, Gerald (2022) Training Neural Networks with Lévy Flight Distribution Algorithm. Lecture Notes on Data Engineering and Communications Technologies.
Full text not available from this repository.Abstract
In machine learning, multilayer perceptrons (MLPs) have long been used as one of the most popular and effective classifiers. With training the crucial process, the susceptibility of conventional algorithms such as back-propagation to get stuck in local optima has encouraged many researchers to opt for metaheuristic algorithms instead. In this paper, we propose a novel population-based metaheuristic algorithm for MLP training using Lévy flight distribution (LFD). In our approach, the optimum weights of the network are found via a population of agents moving through the search space either by Lévy flight motions or by random walks. Comparing the results of this algorithm on several datasets from the UCI repository with other population-based metaheuristic algorithms shows excellent results and superiority of the LFD algorithm.
| Item Type: | Article |
|---|---|
| Subjects: | R Medicine > RZ Other systems of medicine |
| Divisions: | Faculty of Medicine, Health and Life Sciences > School of Medicine |
| Depositing User: | lorestan university |
| Date Deposited: | 01 Oct 2022 07:17 |
| Last Modified: | 01 Oct 2022 07:17 |
| URI: | http://eprints.lums.ac.ir/id/eprint/3940 |
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