Sentence examples for upper level variables from inspiring English sources

In this paper, we introduce bilevel evolutionary algorithm based on quadratic approximations (BLEAQ) of optimal lower level variables with respect to the upper level variables.

Noted that if the dimension of upper level variable is high, generating a uniformly spread upper level variables and refining the resulting upper level variable will be computationally expensive.

This model will evaluate the effect of same level and upper level variables on the outcome leptospirosis, using three blocks.

Since, the effects of distal variables are mediated through proximate variables; lower level variables cannot be a confounder for upper level variables.

A recent study by Eichfelder [8] suggests a refinement-based strategy in which the algorithm starts with a uniformly distributed set of points on upper level variable.

Since it is a difficult task to choose the best choice (x y)) on the Pareto front for a given upper level variable x, our approach in this paper consists to consider the set Ω as a new constraint set for the upper level problem.

Since it is a difficult task to choose the best choice x ( y ) on the Pareto front for a given upper level variable x, our approach in this paper consists of considering the set Ω as a new constraint set for the upper level problem.

Remark 4.1 Observe that for the subdifferential estimate of φ p in Theorem 4.3 ii), the upper bound of the basic subdifferential does not contain the partial derivative of the lower-level objective function f ( x, y, z ) with respect to the upper-level variable x.

For a given upper-level variable x, the weakly efficient solution set Ψ wef ( x ) of the lower-level problem (1.3) is not in general a singleton, hence it is difficult to choose the best point z ( x ) on the set Ψ wef ( x ).

Further, to confirm the robustness of the M-BLEA, we present the final (optimized) upper level design variables attained by both algorithms at termination, in each of the three independent runs.

Generate the initial population (mathrm{Pop}(0)) with population size N comprised by the upper level decision variable.