using landscape measures for the online tuning of heterogeneous distributed gas
Tuning distributed genetic algorithms (dGAs) increases even more the task of finding an appropriate parameterization, since the migration operator adds, at least, five additional values that have to be set up. This work is a preliminary approach on using a landscape measure (the Fitness Distance Correlation) to dynamically adjust one of these five parameters, in particular, the migration period. The results have shown that, by using this information, the quality of the solutions is competitive with those obtained by the algorithms with the pre-tuned migration period, but with a saving of more than 100 hours of preliminary experimentation.
landscape measures tuning distributed genetic algorithms