A Performance Analysis of Compressed Compact Genetic Algorithm

Compressed compact genetic algorithm (cGA) is an algorithm that utilizes the compressed chromosome encoding and compact genetic algorithm (cGA). The advantage of cGA is to reduce the memory usage by representing population as a probability vector. In this paper, we analyze the performance in term of robustness of cGA. Since the compression and decompression strategy employ two parameters, which are the length of repeating value and the repeat count, we vary these two parameters to see the performance affected in term of convergence speed. The experimental results show that cGA outperforms cGA and is a robust algorithm.