Global optimization seeks a minimum or maximum of a multimodal function over a discrete or continuous domain. In this paper, we propose a hybrid heuristic - based on the CGRASP and GENCAN methods - for finding approximate solutions for continuous global optimization problems subject to box constraints. Experimental results illustrate the relative effectiveness of CGRASP-GENCAN on a set of benchmark multimodal test functions.
AT&T Labs Research Technical Report, AT&T Labs Research, Shannon Laboratory, Florham Park, NJ 07932, April 2009.
View Continuous GRASP with a local active-set method for bound-constrained global optimization