Paper Info
Title
Adaptive Differential Evolution with Elite Opposition-Based Learning and its Application to Training Artificial Neural Networks.
Abstract
Differential Evolution (DE) algorithm is one of the popular evolutionary algorithms that is designed to find a global optimum on multi-dimensional continuous problems. In this paper, we propose a new variant of DE algorithm by combining a self-adaptive DE algorithm called dynNP-DE with Elite Opposition-Based Learning (EOBL) scheme. Since dynNP-DE algorithm uses a small number of population size in the later of the search process, the population diversity becomes low, and therefore premature convergence may occur. We have therefore extended an OBL scheme to dynNP-DE algorithm to overcome this shortcoming and improve the optimization performance. By combining EOBL scheme to dynNP-DE algorithm, the population diversity can be supplemented because not only the information of individuals but also their opposition information can be utilized. We measured the optimization performance of the proposed algorithm on CEC 2005 benchmark problems and breast cancer detection, which is a research field that has recently attracted a lot of attention. It was verified that the proposed algorithm could find better solutions than five state-of-the-art DE algorithms.
Year
DOI
Venue
2019
10.3233/FI-2019-1764
FUNDAMENTA INFORMATICAE
Keywords
Field
DocType
Artificial Neural Networks,Differential Evolution Algorithm,Opposition-Based Learning,Feed-Forward Neural Network,Neural Network Training
Discrete mathematics,Opposition based learning,Elite,Differential evolution,Artificial intelligence,Artificial neural network,Mathematics
Journal
Volume
Issue
ISSN
164
2-3
0169-2968
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Tae Jong Choi152.47
Jong-Hyun Lee24410.32
Hee Yong Youn3943142.78
Chang Wook Ahn475960.88