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Given protein features, we use the fitness function to compute scores for all chromosomes in population.
We use the fitness function shown below: (7)fitness = error + γ *number_of_selected_features, where error corresponds to the classification error on the validation data set D v, γ is a trade-off between classification error and the number of selected features.
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The criterion for the best-fit is minimizing the residuals between the observed and synthetic waveforms using the fitness function given by Miyake et al. (1999).
Using the fitness function, the cost of particles is evaluated according to their positions.
The goodness of each individual is then evaluated using the fitness function.
Finally, the best computer model is generated by GP using the fitness function adopted.
This selection uses the fitness function in (21) based on the MMDP which is described below.
The fitness value for each cuckoo egg is computed using the fitness function represented in Eq. (6).
At the second selection stage (Selection-II), the algorithm firstly evaluates the individuals of (mathcal {P}_{new}) using the fitness function f j (3).
Some advantages of GA are as follows: GA uses the fitness function and the stochastic concepts (not deterministic rule) to search for optimal solution.
The proposed algorithm uses gradient matrix to determine whether the trail vector generated by differential evolution algorithm is worth using the fitness function to evaluate it or not.
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