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LZW Chromosome Encoding in Estimation of Distribution Algorithms

LZW Chromosome Encoding in Estimation of Distribution Algorithms

Orawan Watchanupaporn, Worasait Suwannik
Copyright: © 2013 |Volume: 4 |Issue: 4 |Pages: 21
ISSN: 1942-3594|EISSN: 1942-3608|EISBN13: 9781466635562|DOI: 10.4018/ijaec.2013100103
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MLA

Watchanupaporn, Orawan, and Worasait Suwannik. "LZW Chromosome Encoding in Estimation of Distribution Algorithms." IJAEC vol.4, no.4 2013: pp.41-61. http://doi.org/10.4018/ijaec.2013100103

APA

Watchanupaporn, O. & Suwannik, W. (2013). LZW Chromosome Encoding in Estimation of Distribution Algorithms. International Journal of Applied Evolutionary Computation (IJAEC), 4(4), 41-61. http://doi.org/10.4018/ijaec.2013100103

Chicago

Watchanupaporn, Orawan, and Worasait Suwannik. "LZW Chromosome Encoding in Estimation of Distribution Algorithms," International Journal of Applied Evolutionary Computation (IJAEC) 4, no.4: 41-61. http://doi.org/10.4018/ijaec.2013100103

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Abstract

Estimation of distribution algorithm (EDA) can solve more complicated problems than its predecessor (Genetic Algorithm). EDA uses various methods to probabilistically model a group of highly fit individuals. Calculating the model in sophisticated EDA is very time consuming. To reduce the model building time, the authors propose compressed chromosome encoding. A chromosome is encoded using a format that can be decompressed by the Lempel-Ziv-Welch (LZW) algorithm. The authors combined LZW encoding with various EDAs and termed the class of algorithms Lempel-Ziv-Welch Estimation of Distribution Algorithms (LZWEDA). Experimental results show that LZWEDA significantly outperforms the original EDA. Finally, the authors analyze how LZW encoding transforms a fitness landscape.

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