Recently, many researchers have designed neural network architectures with evolutionary algorithms but most of them have used only the fittest solution of the last generation. To better exploit information, an ensemble of individuals is a more promising choice because information that is derived from combining a set of classifiers might produce higher accuracy than merely using the information from the best classifier among them. One of the major factors for optimum accuracy is the diversity of the classifier set. In this paper, we present a method of generating diverse evolutionary neural networks through fitness sharing and then combining these networks by the behavior knowledge space method. Fitness sharing that shares resources if the distance between the individuals is smaller than the sharing radius is a representative speciation method, which produces diverse results than standard evolutionary algorithms that converge to only one solution. Especially, the proposed method calculates the distance between the individuals using average output, Pearson correlation and modified Kullback-Leibler entropy to enhance fitness sharing performance. In experiments with Australian credit card assessment, breast cancer, and diabetes in the UCI database, the proposed method performed better than not only the non-speciation method but also better than previously published methods.
|Number of pages||15|
|Publication status||Published - 2008 Mar|
Bibliographical noteFunding Information:
This work was supported by Brain Science and Engineering Research Program sponsored by the Korean Ministry of Science and Technology. Kyung-Joong Kim (Student Member, IEEE) received the B.S. and M.S. degrees in computer science from the Yonsei University, Seoul, Korea, in 2000 and 2002, respectively. Since 2002, he has been a Ph.D. student in the Department of Computer Science, Yonsei University. His research interests include evolutionary neural network, robot control, and agent architecture. Sung-Bae Cho (Member, IEEE) received the B.S. degree in computer science from the Yonsei University, Seoul, Korea, in 1988 and the M.S. and Ph.D. degrees in computer science from the Korea Advanced Institute of Science and Technology (KAIST), Taejeon, Korea, in 1990 and 1993, respectively. From 1991 to 1993, he worked as a Member of the Research Staff at the Center for Artificial Intelligence Research at KAIST. From 1993 to 1995, he was an Invited Researcher of the Human Information Processing Research Laboratories at ATR (Advanced Telecommunications Research) Institute, Kyoto, Japan. In 1998, he was a Visiting Scholar at the University of New South Wales, Canberra, Australia. Since 1995, he has been a Professor in the Department of Computer Science, Yonsei University. His research interests include neural networks, pattern recognition, intelligent man–machine interfaces, evolutionary computation, and artificial life. Dr. Cho is a Member of the Korea Information Science Society, INNS, the IEEE Computer Society, and the IEEE Systems, Man and Cybernetics Society. He was awarded outstanding paper prizes from the IEEE Korea Section in 1989 and 1992, and another one from the Korea Information Science Society in 1990. In 1993, he also received the Richard E. Merwin prize from the IEEE Computer Society. In 1994, he was listed in Who's Who in Pattern Recognition from the International Association for Pattern Recognition and received the best paper awards at the International Conference on Soft Computing in 1996 and 1998. In 1998, he received the best paper award at the World Automation Congress. He was listed in Marquis Who's Who in Science and Engineering in 2000 and in Marquis Who's Who in the World in 2001.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence