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In order to evaluate the problem of missing data, an additional statistical technique, the multiple imputation technique [ 21, 22], was applied that was specifically developed for analyzing data sets with missing entries.
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A computational code was developed in order to evaluate the equations governing the problem, the boundary, and initial conditions.
In order to evaluate the computation time for solving Problem 1, we consider one artificial example of a BN with 15 states and 3 control inputs.
In order to evaluate the solutions of the multiobjective optimization problem, we need to introduce the notion of Pareto optimality [ 33].
In order to evaluate the proposed optimization approach, single objective test problem, multi-objective I-beam and machine-tool optimization problems taken from the literature are solved.
From the numerical viewpoint we have used two algorithms [36, 38] to compute the eigenvalues of the linearized problem in order to evaluate the conservativeness of our conditions.
In order to evaluate the proposed optimization approach a welded beam design problem taken from the literature is solved.
In order to evaluate the existing process related to the ICIP, we identified the problems and classified them to find their root causes.
In order to evaluate the performance of the ES proposed, instances of increasing problem hardness are considered.
These multi-objective optimization problems were selected in order to evaluate the robustness and versatility of the algorithms studied.
In order to evaluate the performance of the SVM algorithm, we have implemented for the same problem the two other previously discussed classification methods.
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