The termination condition determines when an optimization algorithm is completed. This post describes a new feature in the MOEA Framework where you can specify termination conditions for an algorithm. We now provide built-in support for fixing the maximum number of evaluations or the maximum wallclock time, but other means can be devised.
If you have used the MOEA Framework in the past, you may have noticed it does not support many plotting options. Yes, it has the Diagnostic Tool for plotting the runtime outputs for an algorithm, but it can only display data generated in the tool. If you were running algorithms programatically, there were not plotting options...until today.
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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January 2016
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