  We propose a novel, flexible algorithm for combining together
metaheuristicoptimizers for non-convex optimization problems. Our approach
treatsthe constituent optimizers as a team of complex agents that
communicateinformation amongst each other at various intervals during the
simulationprocess. The information produced by each individual agent can be
combinedin various ways via higher-level operators. In our experiments on
keybenchmark functions, we investigate how the performance of our
algorithmvaries with respect to several of its key modifiable properties.
Finally,we apply our proposed algorithm to classification problems involving
theoptimization of support-vector machine classifiers.
