Design Enhancement of Linkage Learning Genetic Algorithms

This research work examines and verifies the use of subchromosome representations previously introduced to the linkage learning genetic algorithm (LLGA). The subchromosome representation is utilized for effectively lowering the number of building blocks in order to escape from the performance limit implied by the convergence time model for the linkage learning genetic algorithm. A preliminary implementation was developed to realize subchromosome representations in the literature. In this study, we once more examine and verify the use of subchromosomes in the linkage learning genetic algorithm. The experimental results indicate that the representation can improve the performance of the linkage learning genetic algorithm on uniformly scaled problems, and the implementation provides a potential way for the linkage learning genetic algorithm to incorporate prior linkage information when such knowledge exists.