Sensitivity Analysis during the Optimization Process Using Genetic Algorithms

Genetic algorithms (GA) are applied to the solution
of high-dimensional optimization problems. Additionally, sensitivity
analysis (SA) is usually carried out to determine the effect on optimal
solutions of changes in parameter values of the objective function.
These two analyses (i.e., optimization and sensitivity analysis)
are computationally intensive when applied to high-dimensional
functions. The approach presented in this paper consists in performing
the SA during the GA execution, by statistically analyzing the data
obtained of running the GA. The advantage is that in this case
SA does not involve making additional evaluations of the objective
function and, consequently, this proposed approach requires less
computational effort than conducting optimization and SA in two
consecutive steps.

Authors:

• M. A. Rubio
• A. Urquia

Keywords:

• Optimization
• sensitivity
• genetic algorithms
• model calibration.

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