1 research outputs found
Performance Enhancement of Distributed Quasi Steady-State Genetic Algorithm
This paper proposes a new scheme for performance enhancement of distributed
genetic algorithm (DGA). Initial population is divided in two classes i.e.
female and male. Simple distance based clustering is used for cluster formation
around females. For reclustering self-adaptive K-means is used, which produces
well distributed and well separated clusters. The self-adaptive K-means used
for reclustering automatically locates initial position of centroids and number
of clusters. Four plans of co-evolution are applied on these clusters
independently. Clusters evolve separately. Merging of clusters takes place
depending on their performance. For experimentation unimodal and multimodal
test functions have been used. Test result show that the new scheme of
distribution of population has given better performance.Comment: Pages: 09 Figures: 08 Tables: 05, International Journal of Digital
Technology, 2009 pp 52-6