This package contains command-line tools to compute of the empirical attainment function (EAF) in two and three dimensions and to perform EAF-based statistical hypothesis tests.
The empirical attainment function and related tests are used mainly in performance evaluation of evolutionary multiobjective optimisation (EMO) algorithms.
A collection of algorithms for various computational problems related to the hypervolume indicator is provided here, covering problems such as:
These algorithms will eventually be integrated into a single package or library, but for now they are available as separate packages.
The hypervolume indicator is commonly used in the selection and archiving steps of EMO algorithms, as well as in the evaluation of their performance.
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