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.
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© 2003 Springer-Verlag Berlin Heidelberg
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Baugh, J.W., Kumar, S.V. (2003). Asynchronous Genetic Algorithms for Heterogeneous Networks Using Coarse-Grained Dataflow. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_88
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DOI: https://doi.org/10.1007/3-540-45105-6_88
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