Derivative-free hybrid methods in global optimization and applications
in December 2010
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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 i...
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.
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