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The use of numerical optimization techniques to address real-world environmental and engineering problems, and provide decision support to stakeholders is a common strategy (Rios and Sahinidis 2012).
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Since most inverse problems are ill-posed, many popular techniques for solving inverse problems use some sort of numerical optimization.
Evolutionary computation (EC) is a class of numerical optimization techniques using biologically analogous concepts (Fig. 1).
However, maximum-likelihood solutions for these models generally require application of numerical optimization algorithms, which are computationally demanding, and thus infeasible for GWA studies using a single processor.
We emphasize the combination of numerical optimization and geometric knowledge.
For the analysis, we extend a widely used SEIRS model to incorporate age-dependent screening rate profile and apply a gradient-based line search algorithm for ease of numerical optimization.
One interest of numerical optimization is whether the global solution can be obtained by the algorithm applied.
The method is computationally effective and can be used for numerical optimization of sandwich lay-ups common in rail and aerospace engineering.
Then, desirability approach of RSM is used for numerical optimization to obtain maximum depth of penetration.
Evolutionary strategies are stochastic algorithms used for numerical optimization [ 48].
The same software was used for numerical optimization.
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