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.

An improved algorithm for fast motion estimation based on the block matching algorithm (BMA) is presented for use in a block-based video coding system. To achieve enhanced motion estimation performance, we propose an adaptive search pattern length for each iteration for the current macro block (MB). In addition, search points that must be checked are determined by means of directional information from the error surface, thus reducing intermediate searches. The proposed algorithm is tested with several sequences and excellent performance is verified.

A new multilayer artificial neural network learning algorithm based on the pattern search method is proposed. The learning algorithm is designed to provide a very simple and effective means of searching the minima of an objective function directly without any knowledge of its derivatives. We test this algorithm on benchmark problems, such as exclusive-or (XOR), parity and alphabetic character learning problems. For all problems, the systems are shown to be trained efficiently by our algorithm. As a simple direct search algorithm, it can be applied to hardware implementations easily.