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We also defined a resource computation fitness function.
Two main objectives are defined and used by the resource computation fitness function.
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During the computation of fitness values for every PE in the NIRA agent's internal FIFO, the maximum fitness is held in an accumulator along its corresponding PE address.
But, an exact computation of fitness may not be really necessary as long as a proper rank is approximately preserved in the evolution's scheme of the survival of the fittest.
Thus two distinct concepts of relatedness are called for in the computation of fitness effects on rare mutants and of convergence stability.
Hence, we used Eq. (14) to optimize the computations in fitness function evaluation.
In this work we propose an algorithm to evolve a robust speech representation, using a dynamic data selection method for reducing the computational cost of the fitness computation while improving the generalisation capabilities.
Therefore, the simplest GFS design with two fuzzy sets per variable (GFS (222)) yielded the best computation-to-fitness ratio.
To do this we used an already published tree as starting point [ 12], and a matrix of genetic distances among samples, corrected with the best fit molecular substitution model, in the fitness computation.
It implements the fitness computation using a neural network.
Indeed, the fitness computation is generally much faster than the actual feature extraction.
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