Abstract
In recent years, multi-population genetic algorithms (MGAs) have been recognized as being more effective both in speed and solution quality than single-population genetic algorithms (SGAs). Despite of these advantages, the behavior and performance of MGAs, like SGAs, are still heavily affected by a judicious choice of parameters, such as connection topology, migration method, migration interval, migration rate, population number, etc. In this paper, the issue of adapting migration parameters for MGAs is investigated. We examine, in particular, the effect of adapting the migration interval as well as migration rate on the performance and solution quality of MGAs. Thereby, we propose an adaptive scheme to evolve the appropriate migration interval and migration rate for MGAs. Experiments on the 0/1 knapsack problem showed that our approach can compete with MGAs with static migration parameters.
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