Asynchronous genetic algorithms for heterogeneous networks using coarse-grained dataflow

Authors:
John W. Baugh;Sujay V. Kumar
Affiliations:
North Carolina State University, Raleigh, NC;NASA Goddard Space Flight Center, Greenbelt, MD
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Year:
2003

Citing 4
Cited 0

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A framework for multiresolution optimization in a parallel/distributed environment: simulation of hierarchical GAs

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Efficient scheduling of arbitrary task graphs to multiprocessors using a parallel genetic algorithm

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ASPARAGOS An Asynchronous Parallel Genetic Optimization Strategy

Proceedings of the 3rd International Conference on Genetic Algorithms

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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.