Zhan, Z.H., Lin, Y., Chen, N., Gong, Y.-J., Zhong, J.-H., Chung, H.S.H., Li, Y., and Shi, Y.-H. (2011) Evolutionary computation meets machine learning: a survey. IEEE Computational Intelligence Magazine, 6 (4). pp. 68-75. ISSN 1556-603X (doi:10.1109/MCI.2011.942584)
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Publisher's URL: http://dx.doi.org/10.1109/MCI.2011.942584
Evolutionary computation (EC) is a kind of optimization methodology inspired by the mechanisms of biological evolution and behaviors of living organisms. In the literature, the terminology evolutionary algorithms is frequently treated the same as EC. This article focuses on making a survey of researches based on using ML techniques to enhance EC algorithms. In the framework of an ML-technique enhanced-EC algorithm (MLEC), the main idea is that the EC algorithm has stored ample data about the search space, problem features, and population information during the iterative search process, thus the ML technique is helpful in analyzing these data for enhancing the search performance. The paper presents a survey of five categories: ML for population initialization, ML for fitness evaluation and selection, ML for population reproduction and variation, ML for algorithm adaptation, and ML for local search.
|Glasgow Author(s) Enlighten ID:||Li, Professor Yun|
|Authors:||Zhan, Z.H., Lin, Y., Chen, N., Gong, Y.-J., Zhong, J.-H., Chung, H.S.H., Li, Y., and Shi, Y.-H.|
|College/School:||College of Science and Engineering > School of Engineering > Systems Power and Energy|
|Journal Name:||IEEE Computational Intelligence Magazine|