Conditional probability mutation in LZWGA

Authors:
Orawan Watchanupaporn;Worasait Suwannik
Affiliations:
Kasetsart University, Bangkok, Thailand;Kasetsart University, Bangkok, Thailand
Venue:
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
Year:
2010

Citing 3
Cited 1

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Genetic Algorithms in Search, Optimization and Machine Learning
A Technique for High-Performance Data Compression

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LZW Encoding in Genetic Algorithm

International Journal of Applied Evolutionary Computation

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Abstract

LZWGA is an algorithm that combines LZW compression algorithm with genetic algorithm (GA). An LZWGA chromosome can be decompressed to a GA binary-string chromosome. In this paper, we propose a new mutation operator for LZWGA called conditional probability mutation (CPM). In contrast to original LZWGA mutation which randomly changes some values in an individual, CPM takes advantage of the relationship between gene positions. We compare the performance of LZWGA with original mutation and LZWGA with CPM on non random and random version of standard benchmark problems. Furthermore, we vary mutation rate to see its effect in the performance. The experimental results show that our proposed mutation outperforms original LZWGA mutation in non random problems.