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LZW Chromosome Encoding in Estimation of Distribution Algorithms

Author

Listed:
  • Orawan Watchanupaporn

    (Department of Computer Science, Kasetsart University, Sriracha Campus, Thailand)

  • Worasait Suwannik

    (Department of Computer Science, Kasetsart University, Bangkhen Campus, Thailand)

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.

Suggested Citation

  • Orawan Watchanupaporn & Worasait Suwannik, 2013. "LZW Chromosome Encoding in Estimation of Distribution Algorithms," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 4(4), pages 41-61, October.
    • Handle: RePEc:igg:jaec00:v:4:y:2013:i:4:p:41-61
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