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篇目详细内容 |
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【篇名】 |
A survey on algorithm adaptation in evolutionary computation |
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【刊名】 |
Frontiers of Electrical and Electronic Engineering |
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【刊名缩写】 |
Front. Electr. Electron. Eng. |
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【ISSN】 |
2095-2732 |
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【EISSN】 |
2095-2740 |
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【DOI】 |
10.1007/s11460-012-0192-0 |
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【出版社】 |
Higher Education Press and Springer-Verlag Berlin
Heidelberg |
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【出版年】 |
2012 |
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【卷期】 |
7
卷1期 |
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【页码】 |
16-31
页,共
16
页 |
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【作者】 |
Jun ZHANG;
Wei-Neng CHEN;
Zhi-Hui ZHAN;
Wei-Jie YU;
Yuan-Long LI;
Ni CHEN;
Qi ZHOU;
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【关键词】 |
evolutionary algorithm (EA); evolutionary computation (EC); algorithm adaptation; parameter control |
【摘要】 |
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Evolutionary computation (EC) is one of the fastest growing areas in computer science that solves intractable optimization problems by emulating biologic evolution and organizational behave iors in nature. To design an EC algorithm, one needs to determine a set of algorithmic configurations like operator selections and parameter settings. How to design an effective and efficient adaptation scheme for adjusting the configurations of EC algorithms has become a significant and promising research topic in the EC research community. This paper intends to provide a comprehensive survey on this rapidly growing field. We present a classification of adaptive EC (AEC) algorithms from the perspective of how an adaptation scheme is designed, involving the adaptation objects, adaptation evidences, and adaptation methods. In particular, by analyzing the population distribution characteristics of EC algorithms, we discuss why and how the evolutionary state information of EC can be estimated and utilized for designing effective EC adaptation schemes. Two AEC algorithms using the idea of evolutionary state estimation, including the clustering-based adaptive genetic algorithm and the adaptive particle swarm optimization algorithm are presented in detail. Some potential directions for the research of AECs are also discussed in this paper. |
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