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Cultural Algorithms, by imitating the social behavior of human communities are able to attain good results while exhibiting a remarkable frugality in resources. This paper explores their needs in terms of sample size and how these needs are affected by the complexity of the information being addressed, with a somewhat surprising result: less is more, smaller sample sizes conduce to better results at any level of complexity.
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