Download e-book for iPad: Differential evolution : a practical approach to global by Kenneth Price, Rainer M. Storn, Jouni A. Lampinen

By Kenneth Price, Rainer M. Storn, Jouni A. Lampinen

ISBN-10: 3540313060

ISBN-13: 9783540313069

Problems challenging globally optimum recommendations are ubiquitous, but many are intractable once they contain limited features having many neighborhood optima and interacting, mixed-type variables.

The differential evolution (DE) set of rules is a pragmatic method of international numerical optimization that's effortless to appreciate, basic to enforce, trustworthy, and speedy. jam-packed with illustrations, computing device code, new insights, and sensible recommendation, this quantity explores DE in either precept and perform. it's a beneficial source for pros desiring a confirmed optimizer and for college students short of an evolutionary standpoint on international numerical optimization.

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Extra info for Differential evolution : a practical approach to global optimization

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For example, the initial value (g = 0) of the jth parameter of the ith vector is x j ,i ,0 = rand j (0,1) ⋅ (b j ,U − b j ,L ) + b j ,L . , 0 ≤ randj(0,1) < 1. The subscript, j, indicates that a new random value is generated for each parameter. Even if a variable is discrete or integral, it should be initialized with a real value since DE internally treats all variables as floating-point values regardless of their type. 3 Mutation Once initialized, DE mutates and recombines the population to produce a population of Np trial vectors.

2 Local Versus Global Optimization f (x ) = D −1 ¦α x 2 i i . 24) i =0 If, however, the hyper-ellipsoid is rotated in all dimensions, it becomes impossible to optimize one parameter independent of the others. This parameter dependence is often referred to as epistasis, an expression from biology (www 01). Salomon (1996) shows that unless an optimizer addresses the issue of parameter dependence, its performance on epistatic objective functions will be seriously degraded. This important issue is discussed extensively in Sect.

2 3 7 1 1 6 0 8 4 0 5 Vector no. 1 of the old population is marked so that it survives into the next population. x1 Fig. 29. Selection. This time, the trial vector loses. 30 presents pseudo-code for DE’s most basic idea. , Np ui = xr3 + F*(xr1 - xr2); if (f(ui) <= f(xi)) { yi = ui; } else { yi = xi; } } }//end while ... Fig. 30. Pseudo-code for a simplified form of DE’s generate-and-test operations Even though the scheme described above already works remarkably well, DE’s performance can be improved and its methodology adapted to a wide variety of optimization scenarios.

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Differential evolution : a practical approach to global optimization by Kenneth Price, Rainer M. Storn, Jouni A. Lampinen


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