Digital Pattern Search and Its Hybridization with Genetic Algorithms for Bound Constrained Global Optimization

Nam-Geun KIM  Youngsu PARK  Jong-Wook KIM  Eunsu KIM  Sang Woo KIM  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E92-A   No.2   pp.481-492
Publication Date: 2009/02/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E92.A.481
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Numerical Analysis and Optimization
Keyword: 
genetic algorithm,  pattern search method,  digital pattern search,  biped walking,  

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Summary: 
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.