Model selection for support vector machines using an asynchronous parallel evolution strategy

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.