Abstract
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.
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