Clustering-based hierarchical genetic algorithm for complex fitness landscapes

We propose the use of a hierarchical Genetic Algorithm (GA) for optimization in complex landscapes. While the slave GA tries to find local optima in the restricted fitness landscape of low complexity, the master GA tries to identify interesting regions in the entire landscape. The slave GA is a conventional GA with high convergence. The master GA is more exploratory in nature. This GA clusters the fitness landscape with each cluster in control of a slave GA. The number of clusters decreases with time to get global characteristics. The novelty of the suggested approach lies in the tradeoff between the search for global optima and convergence to local optima that can be controlled between the two GAs. We tested the algorithm and observed that the approach exceeds conventional GA as well as Particle Swarm Optimization in complex landscapes.