K. Maier, U. Hönig, and W. Schiffmann (Germany)
real-valued parameter optimization, parallel and distributed computing, volatile resources, fault tolerance
In engineering applications often real-valued performance functions must be optimized. If the performance function is discontinuous, there exist no derivations with respect to the parameters that should be optimized. Thus, we can only use undirected search methods. In contrast to simple trial and-error approaches, evolutionary strategies can extract population-based information that guide the search process and help to improve the quality of the results. The paper describes the parallel implementation of an state-of-the-art evolutionary strategy which uses the covariance adaptation operator. It proposes a communication topology for main taining selective pressure and a master-slave scheme for fault tolerance in distributed environments which are com posed of volatile resources. Experimental results demon strate how the implementation can be adapted to specific needs and how the parallel implementation behaves in the case of resource failures.
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