Asynchronous Genetic Algorithms for Heterogeneous Networks Using Coarse-Grained Dataflow

John W. Baugh Jr.¹ and Sujay V. Kumar²

¹North Carolina State University,
Raleigh, NC 27695 USA
john.baugh@ncsu.edu

²NASA Goddard Space Flight Center,
Greenbelt, MD 20771 USA
sujay@hsb.gsfc.nasa.gov

Abstract. Genetic algorithms (GAs) are an attractive class of techniques for solving a variety of complex search and optimization problems. Their implementation on a distributed platform can provide the necessary computing power to address large-scale problems of practical importance. On heterogeneous networks, however, the performance of a global parallel GA can be limited by synchronization points during the computation, particularly those between generations. We present a new approach for implementing asynchronous GAs based on the dataflow model of computation – an approach that retains the functional properties of a global parallel GA. Experiments conducted with an air quality optimization problem and others show that the performance of GAs can be substantially improved through dataflow-based asynchrony.

LNCS 2723, p. 730 ff.

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