In this article the effects of altering the rate and amount of learning on the Baldwin effect are examined. Using a version of the abstract tunable NK model, it is shown that the adaptation process is sensitive to the rate of learning, particularly as the correlation of the underlying fitness landscape varies. Typically a high learning rate proves most beneficial as landscape correlation decreases. It is also shown that the amount of learning can have a significant effect on the adaptation process, where increased amounts of learning prove beneficial under higher learning rates on uncorrelated landscapes.

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