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The application of a parallel evolutionary algorithm (ES) to model selection for support vector machines is examined. The problem of model selection is a computationally intense non-convex optimization problem. For this reason a parallel search strategy is desirable. A new non-blocking asynchronous ES is developed for this task. The algorithm is tested on five standard test sets optimizing a number of heuristic bounds on the expected generalization error.
Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on (Volume:1 )
Date of Conference: 14-17 Dec. 2003