Abstract
Genetic algorithms are search or classification algorithms based on natural models. They present a high degree of internal parallelism. We developed two versions, differing in the way the population is organized and we studied and compared their characteristics and performances when applied to the optimization of multidimensional function problems. All the implementations are realized on transputer networks.
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