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This work again suggests that different mutation rates may be optimal under different circumstances; moreover, it suggests that even if a value is optimal, it may not be reachable via a natural selection algorithm operating on a highly rugged landscape.
Equally important, Earl and Deem's study showed that different mutation rates can be selected for in different environments; this implies that there is not necessarily an optimum mutation rate, but rather that different evolvability characteristics can be optimal under different circumstances.
In the presence of such trade-offs, different genotypes may be optimal under different densities.
However, there is no reason that variety-generating properties should not respond to selection, just as any other trait, and different variety-generating properties would be optimal under different environmental conditions.
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Given such wider range of possible response dynamics, the questions arise which of these are optimal under different circumstances, and how evolution could result in the emergence and transitions among the molecular mechanisms implementing different response dynamics.
The results with different numbers of UAS repeats indicate that in Arabidopsis the 5XUAS is optimal under the experimental methods described.
Thus, for different questions, different encoding strategies will be optimal.
Under these conditions, combining data sets from different origins may not be optimal to improve estimations of QTL localisations and effects.
Since they appeal to different types of people, we thought it would be optimal to combine them.
For AWGN channel, these techniques are optimal techniques, based on different assumptions and detailed algorithms under different application scenarios.
The questions we ask are rather "How well are different approaches suited to attain this goal?" and "What are optimal settings under which such studies can be successful?".
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