| Recent Advances in Evolutionary Computation |
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| 作者姓名： | Xin Yao and Yong Xu |
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| 作者单位： | [1]Nature Inspired Computation and Applications Laboratory, The University of Science and Technology of China Hefei 230027, P.R. China [2]The Centre of Excellence for Research in Computational Intelligence and Applications （CERCIA）School of Computer Science, The University of Birmingham, Edgbaston, Birmingham B15 2TT, UK |
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| 基金项目： | This work is partially supported by the National Natural Science Foundation of China （Grant No. 60428202）, and the Advantage West Midlands, UK. |
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| 摘 要： | Evolutionary computation has experienced a tremendous growth in the last decade in both theoretical analyses and industrial applications. Its scope has evolved beyond its original meaning of ＂biological evolution＂ toward a wide variety of nature inspired computational algorithms and techniques, including evolutionary, neural, ecological, social and economical computation, etc, in a unified framework. Many research topics in evolutionary computation nowadays are not necessarily ＂evolutionary＂. This paper provides an overview of some recent advances in evolutionary computation that have been made in CERCIA at the University of Birmingham, UK. It covers a wide range of topics in optimization, learning and design using evolutionary approaches and techniques, and theoretical results in the computational time complexity of evolutionary algorithms. Some issues related to future development of evolutionary computation are also discussed.
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| 关 键 词： | 进化计算 神经网络 复杂性 生物进化 进化算法 |
| 收稿时间： | 2005-08-18 |
| 修稿时间： | 2005-08-18 |
Recent Advances in Evolutionary Computation |
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| Xin Yao and Yong Xu.Recent Advances in Evolutionary Computation[J].Journal of Computer Science and Technology,2006,21(1):1-0. |
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| Authors: | Xin Yao Yong Xu |
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| Abstract: | Evolutionary computation has experienced a tremendous growth in the last decade in both theoretical analyses and industrial applications. Its scope has evolved beyond its original meaning of "biological evolution" toward a wide variety of nature inspired computational algorithms and techniques, including evolutionary, neural, ecological, social and economical computation, etc., in a unified frarnework. Many research topics in evolutionary computation nowadays are not necessarily "evolutionary". This paper provides an overview of some recent advances in evolutionary computation that have been made in CERCIA at the University of Birmingham, UK. It covers a wide range of topics in optimization, learning and design using evolutionary approaches and techniques, and theoretical results in the computational time complexity of evolutionary algorithms. Some issues related to future development of evolutionary computation are also discussed. |
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| Keywords: | evolutionary computation neural network ensemble prisoner's dilemma real-world application computational time complexity |
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