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The composition of useful approximate fitness functions from physiological and allocational responses is a major challenge, with some leads originating from the model.
They might also allow the computation of an approximate fitness function, which determines the effect of any particular genotypic change on fitness-related properties.
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Utilizing this memory, the algorithm can approximate the fitness landscape before actual fitness evaluation for some unsuitable solutions.
In these applications efficient strategies to approximate the fitness function have to be adopted, looking for a trade-off between optimization performance and efficiency.
Surrogate-assisted evolutionary optimization has proved to be effective in reducing optimization time, as surrogates, or meta-models can approximate expensive fitness functions in the optimization run.
Because of this, we may approximate the fitness of an organism from its growth rate in a set of static environments.
As a result, we can approximate the fitness benefits enjoyed by a new mutation that we introduce after a previous male-specific modifier has reached fixation (see eq.38 of Appendix S1).
To approximate the fitness distribution on a given tree, we will solve this equation numerically.
We approximate the fitness reward for each replicate as a function of the arrival time that steps down from one to zero after a time limit.
This means users are given a single overarching metric to evaluate how much exercise they really got, rather than just approximating their fitness by measuring overall steps.
The utility of FBA relies on the assumption that growth rate approximates overall fitness and is the primary focus of selection.
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