Performance Enhancement of Distributed Quasi Steady-State Genetic Algorithm
This work addresses performance improvement for distributed genetic algorithms, which is incremental as it builds on existing methods with specific modifications.
The paper tackles performance enhancement of distributed genetic algorithms by dividing the initial population into female and male classes, using distance-based clustering and self-adaptive K-means for reclustering, and applying co-evolution plans on clusters independently, with results showing better performance on unimodal and multimodal test functions.
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.