期刊
MATHEMATICS
卷 8, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/math8010069
关键词
neuroevolution; differential evolution; neural networks
类别
资金
- Progetti Ricerca di Base 2015-2019 Baioletti-Milani-Poggioni by Department of Mathematics and Computer Science University of Perugia, Italy
In this paper, a Neural Networks optimizer based on Self-adaptive Differential Evolution is presented. This optimizer applies mutation and crossover operators in a new way, taking into account the structure of the network according to a per layer strategy. Moreover, a new crossover called interm is proposed, and a new self-adaptive version of DE called MAB-ShaDE is suggested to reduce the number of parameters. The framework has been tested on some well-known classification problems and a comparative study on the various combinations of self-adaptive methods, mutation, and crossover operators available in literature is performed. Experimental results show that DENN reaches good performances in terms of accuracy, better than or at least comparable with those obtained by backpropagation.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据