LZW Chromosome Encoding in Estimation of Distribution Algorithms

Orawan Watchanupaporn and Worasait Suwannik
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Orawan Watchanupaporn: Department of Computer Science, Kasetsart University, Sriracha Campus, Thailand
Worasait Suwannik: Department of Computer Science, Kasetsart University, Bangkhen Campus, Thailand

International Journal of Applied Evolutionary Computation (IJAEC), 2013, vol. 4, issue 4, 41-61

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.

Date: 2013
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