Digital Pattern Search and Its Hybridization with Genetic Algorithms for Bound Constrained Global Optimization
Nam-Geun Kim, Youngsu Park, Jong-Wook Kim, Eun‐Su Kim, Sang Woo Kim · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · 2009
In this paper, we present a recently developed pattern search method called Genetic Pattern Search algorithm (GPSA) for the global optimization of cost function subject to simple bounds. GPSA is a combined global optimization method using genetic algorithm (GA) and Digital Pattern Search (DPS) method, which has the digital structure represented by binary strings and guarantees convergence to stationary points from arbitrary starting points. The performance of GPSA is validated through extensive numerical experiments on a number of well known functions and on robot walking application. The optimization results confirm that GPSA is a robust and efficient global optimization method.