Abstract
genetic programming to evolve models that predict the
throughput in disk arrays. The results are compared to
previous hand-crafted analytical and
automatically-generated interpolation-based device
models. An analysis is performed to investigate the
optimality of the run parameters chosen as well as to
discover whether the approach has the tendency to
overfit its training data. The process is shown to find
models that outperform both recently published and
currently used models and to be sensitive to population
size but not run length.
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