Sensitivity analysis is the "study of how the uncertainty in the output of a mathematical model or system can be apportioned to different sources of uncertainty in its inputs" [Wikipedia]. Sensitivity analysis is a powerful technique for gaining insight into a model by understanding in general terms how the model's output is influenced by the model's inputs. In this post, we provide a short introduction to sensitivity analysis using visual scatter plots followed by an introduction of SALib, an open source Python library for sensitivity analysis.
In the last post, we discussed how to introduce new problems to the MOEA Framework. Researchers developing new multi-objective optimization algorithms may also find it convenient to implement their algorithm within the MOEA Framework for several reasons:
The MOEA Framework is a Java library for multiobjective optimization. While it comes with an assortment of optimization algorithms and test problems, its true power lies in its ability to solve user-defined problems. This post details the steps required to define new optimization problems within the MOEA Framework.
Suppose we have two datasets, which are typically called populations. We want to determine if the two datasets are similar in some way, such as having the same population mean. We could compare the mean value of each dataset, but what happens if one of the dataset, by random chance, contained unusually large values. We might incorrectly assert the datasets are different even if the underlying processes that generated the two datasets are identical. To avoid this potential problem, we turn to statistical tests to provide more rigor.
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Keyboard Scientist is a blog about conducting science in a digital world, discussing topics ranging from programming, model design and analysis, and publishing. In addition, we post a range of topics concerning our open source optimization framework. Subscribe
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