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Number of decision variables.
D: number of decision variables.
(N) is the number of decision variables.
In this paper, the number of decision variables is 82.
The global minima of error rate with the change in the number of decision trees may be taken as the optimal number of decision trees.
In second part, by considering number of decision variables, the objective function is optimized.
Such models require extensive computational effort that increases drastically as the number of decision variables increases.
Performance of FDG optimization method may degrade for the large number of decision variables.
The input parameter ranges and counts do not increase with the number of decision makers.
Table 2 shows the number of decision variables of respective dimension and their percentage.
The main disadvantage of the mentioned technique is its dependency on the number of decision variables.
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