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Typically, the performance of swarm and evolutionary methods is assessed by comparing their results when applied to some known finite benchmarks. In general, these metaheuristics depend on many parameter values and many possible exchangeable sub-steps, which yields a huge number of possible algorithm configurations. In this paper we argue that this high setup versatility lets developers expressively...
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