Title :
Improving the performance of LZWGA by using a new mutation method
Author :
Numnark, Somrak ; Suwannik, Worasait
Author_Institution :
Fac. of Sci., Kasetsart Univ., Bangkok
Abstract :
LZW encoding in Genetic Algorithm (LZWGA) encodes a chromosome in a format that can be decompressed by Lempel-Ziv-Welch (LZW) algorithm. This encoding reduces the size of the chromosome and enabled the algorithm to solve a very large problem. This paper proposes a novel mutation in LZWGA. The result shows that the new method can solve OneMax and Trap problem 46.3% faster. Moreover, this method can reduce the size of the compressed chromosome by 54.8%.
Keywords :
encoding; genetic algorithms; Lempel-Ziv-Welch algorithm; Lempel-Ziv-Welch encoding; compressed chromosome; genetic algorithm; mutation method; Algorithm design and analysis; Compression algorithms; Data compression; Data structures; Dictionaries; Evolutionary computation; Genetic mutations; Heuristic algorithms; Resists; Testing;
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
DOI :
10.1109/CEC.2008.4631042
