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Distributed island-model genetic algorithms using heterogeneous parameter settings

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2 Author(s)
Yiyuan Gong ; Graduate School of Arts and Sciences, University of Tokyo ; Alex Fukunaga

Achieving good performance with a parallel genetic algorithm requires properly configuring control parameters such as mutation rate, crossover rate, and population size. We consider the problem of setting control parameter values in a standard, island-model distributed genetic algorithm. As an alternative to tuning parameters by hand or using a self-adaptive approach, we propose a very simple strategy which statically assigns random control parameter values to each processor. Experiments on benchmark problems show that this simple approach can yield results which are competitive with homogeneous distributed genetic algorithm using parameters tuned specifically for each of the benchmarks.

Published in:

2011 IEEE Congress of Evolutionary Computation (CEC)

Date of Conference:

5-8 June 2011