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In recent years largescale 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 highdimensional nonconvex and/or nonsmooth optimization problems. This is the reason of this book why to develop and study new algorithms for solving largescale 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 largescale problems. The study of new algorithms which allow one to solve largescale 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 derivativefree methods benefit the largescale GO problems. This book presents several derivativefree hybrid methods for largescale GOproblems, & applied to data mining, biochemstry, biomedicine.
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