In recent years large-scale global optimization (GO) problems have drawn considerable attention. These problems have many applications, in particular in data mining, computational biology, computational chemistry, and medicine.Numerical methods for GO are often very time consuming and could not be applied for high-dimensional non-convex and/or non-smooth optimization problems. This is the reason of this book why to develop and study new algorithms for solving large-scale GO problems.The existing local/global optimization techniques effectively solve many problems when the number of variables is not very large and, as a rule, fail to solve many large-scale problems. The study of new algorithms which allow one to solve large-scale GO problem is very important. One technique is to use hybrid of global and local/global search algorithms. When the gradient (or its generalizations) of theobjective functions and the constraint functions are very complex in form or they are not known, the derivative-free methods benefit the large-scale GO problems. This book presents several derivative-free hybrid methods for large-scale GOproblems, & applied to data mining, biochemstry, biomedicine.

This book (dedicated to his father) was written based on Dr Jiapu Zhang's PhD thesis. Dr Jiapu Zhang was born in 1971 in Fei County, Shandong, P.R.China. He got his bachelor degree in 1993 and 1st masters degree in research in 1996 from China, 2nd masters degree in research in 2000 from National University of Singapore, and 1st postdoc from CSIRO.

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