Читать книгу Power Magnetic Devices - Scott D. Sudhoff - Страница 34

Many classical optimization methods are very effective if they initialized to be close to the solution. This is because many functions are “locally” convex. At the same time, while GAs are often very effective at getting close to a global optimum, they may not always converge rapidly from a good approximation of a solution to the exact solution. This suggests a combination of the two approaches, wherein the best individual of the final population is used to initialize a classical optimization method. To this end, the Nelder–Mead simplex method [1] is particularly attractive because it does not require gradients or Hessians. In performing such an optimization, it is normally the case that in problems with a mixed search space, those genes that represent discrete choices are held fixed.