Automatic starting point selection for function optimization
β Scribed by S. P. Brooks; B. J. T. Morgan
- Book ID
- 104639795
- Publisher
- Springer US
- Year
- 1994
- Tongue
- English
- Weight
- 514 KB
- Volume
- 4
- Category
- Article
- ISSN
- 0960-3174
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β¦ Synopsis
Traditional (non-stochastic) iterative methods for optimizing functions with multiple optima require a good procedure for selecting starting points. This paper illustrates how the selection of starting points can be made automatically by using a method based upon simulated annealing. We present a hybrid algorithm, possessing the accuracy of traditional routines, whilst incorporating the reliability of annealing methods, and illustrate its performance for a particularly complex practical problem.
π SIMILAR VOLUMES
This approach aims to optimize the kernel parameters and to efficiently reduce the number of support vectors, so that the generalization error can be reduced drastically. The proposed methodology suggests the use of a new model selection criterion based on the estimation of the probability of error