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Motivated by above discussions this paper propose a novel algorithm which can decrease the number of evolution generation, and can also evolve the fuzzy system for obtaining a better performance.
Figure 8 The impact of M. From Figure 8, we can see that when the population size increases, the number of evolution iteration declines, which means that the global search ability enhances and the convergence time will fall.
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The time interval required by the method is considered as fictitious and the number of evolutions carried out at each iteration must be defined.
Where f t is the average of the fitness values of all the individuals in the first front; tmax is the maximum number of evolution generations.
When the specified number of evolution generation is reached, the chromosome whose objective function value is minimal in the whole population is output as the global optimal solution.
When the specified number of evolution generation is reached, the individual whose objective value is smallest in the whole population (not in the subpopulation) is output as the optimal solution of the network reconfiguration problem.
If building a sophisticated structure like an eye increases the number of children produced, evolution may well build an eye.
But if destroying a sophisticated structure like the eye increases the number of children produced, evolution will just as happily destroy the eye.
The number of level 4 evolution rejecters was too small for statistical analysis of their religions.
So, what drives the almost universally occurring increase in the number of genes during evolution of our virtual organisms?
The following formula is used to obtain an overall accuracy score: Aveleft( {S_{i},g} right) = mathop sum limits_{v = 1}^{j} Accleft( {S_{ij},g} right) (2 where j is the total number of evolutions in the physical schema of i.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com