Asynchronous Parallel Evolutionary Model Selection for Support Vector Machines

Abstract

The application of a parallel evolutionary strategy (ES) to model selection for support vector machines is examined. The problem of model se- lection 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 flve standard test sets optimizing a number of heuristic bounds on the expected generalization er- ror. Furthermore, the algorithm is used to select edit-distances for chromosomal data.