Simulated Evolution and Learning
School of Computer Science The University of Birmingham Edgbaston, Birmingham B15 2TT, U. K.URL: http://www.cs.bham.ac.uk/˜xin
Evolution and learning are two fundamental forms of adaptationl,2). Simulated evolution and learning refers to the study of techniques and methods inspired by Nature for solving complex and difficult real-world problems. These techniques and methods include evolutionary algorithms3), fuzzy learning algorithms, neural learning algorithms, and various statistical learning methods such as nearest neighbor classifiers. In addition to various learning tasks, these techniques and methods have also been applied to various difficult optimization problems that cannot be solved effectively by classical methods (such as mathematical programming methods). This special issue contains six papers selected from those presented at the Second Asia-Pacific Conference on Simulated Evolution And Learning (SEAL’98), Canberra, Australia, 24-27 November 1998. However, all six papers have been rereviewed and substantially extended and revised. They represent significant improved work from their original SEA L’98 papers. The six papers can be grouped into three categories. The first two papers by He et al. and by Ishibuchi and Nakashima described novel applications of genetic algorithms to nearest neighbor classifiers. The next two papers by Kawakami et al. and by Tachibana and Furuhashi presented new fuzzy learning systems. The last two papers by Myung and Kim and by Yu and Wu discussed constrained optimization using the evolutionary approach. I would like to take this opportunity to thank Dr Bob McKay, the SEAL’98 Organizing Committee Chair, for playing a pivotal role in organizing the very successful SEAL’98, Professor Kaoru Hirota, the Editor-in-Chief of the Journal of Advanced Computational Intelligence, for encouraging me to edit this special issue, and all the authors for their high-quality work. References: 1)X. Yao, J-H. Kim, and T. Furuhashi, eds., Simulated Evolution and Learning, Vol. 1285 of Lecture Notes in Artificial Intelligence. Berlin, Germany: Springer-Verlag, 1997. 2)B. Mckay, X. Yao, C. S. Newton, J-H. kim, and T. Furuhashi, eds., Simulated Evolution and Learning, Vo1.1585 of Lecture Notes in Artificial Intelligence. Berlin, Germany: Springer-Verlag, 1999. 3)X. Yao, ed., Evolutionary Computation: Theory and Applications. Singapore: World Scientific Publishing Co., 1999.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 International License.