NDLI: Hybridisation of evolutionary programming and machine learning with k-nearest neighbor estimation

Content Provider	IEEE Xplore Digital Library
Author	Jingsong He Zhenyu Yang Xin Yao
Copyright Year	2007
Description	Author affiliation: Univ. of Sci. & Technol. of China (USTC), Hefei (Jingsong He; Zhenyu Yang; Xin Yao)
Abstract	Evolutionary programming (EP) focus on the search step size which decides the ability of escaping local minima, however does not touch the issue of search in promising region. Estimation of distribution algorithms (EDAs) focus on where the promising region is, however have less consideration about behavior of each individual in solution search algorithms. Since the basic ideas of EP and EDAs are quite different, it is possible to make them reinforce each other. In this paper, we present a hybrid evolutionary framework to make use of both the ideas of EP and EDAs through introducing a mini estimation operator into EP's search cycle. Unlike previous EDAs that use probability density function (PDF), the estimation mechanism used in the proposed framework is the k-nearest neighbor estimation which can perform better with relative small amount of training samples. Our experimental results have shown that the incorporation of machine learning techniques, such as k-nearest neighbor estimation, can improve the performance of evolutionary optimisation algorithms for a large number of benchmark functions.
Starting Page	1693
Ending Page	1700
File Size	245185
Page Count	8
File Format	PDF
ISBN	9781424413393
DOI	10.1109/CEC.2007.4424677
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-25
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic programming Machine learning Electronic design automation and methodology Genetic mutations Optimization methods Machine learning algorithms Helium Gaussian distribution Accuracy Evolutionary computation
Content Type	Text
Resource Type	Article

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