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In GA implementation, a fitness function is used to evaluate the chromosomes for reproduction.
An objective, called fitness function, is used to evaluate the quality of each chromosome.
The fitness function (equivalent to objective function) is used to evaluate the "fitness" of each individual in every generation.
A fitness function is used to evaluate the goodness of each chromosome in terms of total tool-indexing time.
A profit function is used to evaluate the QoS of the chosen serving node, and a cost function is used to model the signaling cost.
In each iteration, which is also called a generation, a fitness function is used to evaluate the fitness of individuals to find the best solution.
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Two objective functions are used to evaluate the design scheme.
The properties of complex variable functions are used to evaluate the stress function.
These functions are used to evaluate the exploitation and convergence rate of the adopted algorithms.
Next, a more expressive real-valued cost function, referred to as the scaled cost function, is used to evaluate clustering.
The Fugl-Meyer assessment, Action Research Arm test and simple test for evaluating hand function were used to evaluate upper-limb function.
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