Reproducible Roulette Wheel Sampling for Message Passing Environments
Roulette Wheel Sampling, sometimes referred to as Fitness Proportionate Selection, is a method to sample from a set of objects each with an associated weight. This paper introduces a distributed version of the method designed for message passing environments. Theoretical bounds are derived to show that the presented method has better scalability than naive approaches. This is verified empirically on a test cluster, where improved speedup is measured. In all tested configurations, the presented method performs better than naive approaches. Through a renumbering step, communication volume is minimized. This step also ensures reproducibility regardless of the underlying architecture.
KeywordsGenetic algorithms Roulette wheel selection Sequential Monte Carlo HPC Message passing
Part of the work presented in this paper was funded by Johnson & Johnson.
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