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First, an adaptive selection of the sample size for the periodic fitness evaluation of a trial solution based on the fitness variance in its local neighborhood is proposed.
The performance measures for comparison of various algorithms such as mean fitness, variance of fitness, and best fitness are computed.
All of the techniques employed are suitable for controller tuning, however, the multiple cooperative and coevolving swarms based results are considerably better in terms of mean fitness, variance of fitness, and success rate in finding a feasible solution in comparison to those obtained using single swarm based methods.
We stop the algorithm when, in the current iteration, the fitness variance is lower than 0.01.
The hit-rate (R2) of the new correlation was 0.9799 and the fitness variance for the new correlation was 0.012.
Several stopping criteria exist in literature, mainly regarding the number of iterations and the fitness variance in the population.
Also, the hit-rate (R2) of the new correlation was 0.9799 and the fitness variance was 0.012.
The hit-rate (R2) of the best genetic program (the new correlation), was 0.9799 and the fitness variance was 0.012.
The two parameters used as fitness measurements are the hit-rate (R2) of the best genetic program and the fitness variance.
Discipulus creates thousands of models (programs) from given data files that allow us to predict outputs from similar inputs and for each model (program) gives us its performance [the hit-rate (R2) and the fitness variance].
At the end of the run, we choose the best model (program) depending on its hit-rate (R2) and fitness variance to calculate the solution gas oil ratio; Rs. Figure 6 shows the fitness improvement of the best genetic program for our correlation with time.
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