Presentation 2005/6/17
Crossover of Real-coded Genetic Algorithms Using Variable Distribution Function
Hiroki NAKANISHI, Hiroshi KINJO, Kunihiko NAKAZONO, Tetsuhiko YAMAMOTO,
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Abstract(in English) Genetic algorithms (GAs) are widely used in solving optimization problems. In this paper, we present a crossover method used in real-coded GAs for neural network training problems. A well-known crossover operator of real-coded GAs is the blend crossover (BLX). The BLX utilizes a uniform probability density function for producing offspring. The BLX has good searching ability for the neural network training. However, the BLX is not fast. In order to improve the searching performance for network training, we propose a biased probability density functions (BPDF) for offspring production process. Simulation shows that the use of the BLX with BPDF effectively improves search performance.
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Keyword(in English) neural network training / real-coded genetic algorithm / blendcrossover / variable distribution function
Paper # NC2005-29
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Committee NC
Conference Date 2005/6/17(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Crossover of Real-coded Genetic Algorithms Using Variable Distribution Function
Sub Title (in English)
Keyword(1) neural network training
Keyword(2) real-coded genetic algorithm
Keyword(3) blendcrossover
Keyword(4) variable distribution function
1st Author's Name Hiroki NAKANISHI
1st Author's Affiliation Graduate school of Engineering and Science, University of the Ryukyus()
2nd Author's Name Hiroshi KINJO
2nd Author's Affiliation Faculty of Engineering, University of the Ryukyus
3rd Author's Name Kunihiko NAKAZONO
3rd Author's Affiliation Faculty of Engineering, University of the Ryukyus
4th Author's Name Tetsuhiko YAMAMOTO
4th Author's Affiliation Faculty of Engineering, University of the Ryukyus
Date 2005/6/17
Paper # NC2005-29
Volume (vol) vol.105
Number (no) 131
Page pp.pp.-
#Pages 4
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