Research Article | OPEN ACCESS
Improved Estimation of Distribution Algorithms Based on Gaussian Distribution
1Shang Gao, Ling Qiu and 3Cungen Cao
1School of Computer Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212003, China
2Artificial Intelligence of Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong 643000, China
3Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China
Research Journal of Applied Sciences, Engineering and Technology 2013 10:1841-1845
Received: November 19, 2012 | Accepted: January 14, 2013 | Published: July 20, 2013
Abstract
Estimation of Distribution Algorithms (EDAs) is a new kind of evolution algorithm. In EDAs, through the statistics of the information of selected individuals in current group, the probability of the individual distribution in next generation is given and the next generation of group is formed by random sampling. An improved estimation of distribution algorithms based on normal distribution is presented for function optimization in continuous space. The algorithm regarded the selected individual as a normal distribution and the random new populations of normal distribution were generated and some selection of individual are crossed with the best solution. Compared with estimation of distribution algorithms based on uniform distribution and estimation distribution algorithm based on normal distribution, the improved estimation distribution algorithm based on normal distribution is more effective through result. At last, better population selection proportions are analyzed.
Keywords:
Continuous space optimization, estimation distribution algorithm, normal distribution, uniform distribution,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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