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I've been playing with parallel processing of genetic algorithms to improve performance but I was wondering what some other commonly used techniques are to optimize a genetic algorithm?

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Since fitness values are frequently recalculated (the diversity of the population decreases as the algorithm runs), a good strategy to improve the performance of a GA is to reduce the time needed to calculate the fitness. Details depend on implementation, but previously calculated fitness values can often be efficiently saved with a hash table. 

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