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moocore: Core Algorithms for Multi-Objective Optimization
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   API reference <reference/index>
   Examples <auto_examples/index>
   whatsnew/index

**Version**: |version| (See :ref:`whatsnew`)

**Date** |today|

**Useful links**:
`Source Repository <https://github.com/multi-objective/moocore>`_ |
`Issue Tracker <https://github.com/multi-objective/moocore/issues>`_

This webpage documents the ``moocore`` Python package. There is also a ``moocore`` `R package <https://multi-objective.github.io/moocore/r>`_.

The goal of the **moocore** project (https://github.com/multi-objective/moocore/) is to collect fast implementations of core mathematical functions and algorithms for multi-objective optimization and make them available to different programming languages via similar interfaces. These functions include:

* Identifying and filtering dominated vectors.
* Quality metrics such as (weighted) hypervolume, epsilon, IGD, etc.
* Computation of the Empirical Attainment Function. The empirical attainment function (EAF) describes the probabilistic distribution of the outcomes obtained by a stochastic algorithm in the objective space.

Most critical functionality is implemented in C, with the R and Python packages providing convenient interfaces to the C code.

**Keywords**: empirical attainment function, summary attainment surfaces, EAF
differences, multi-objective optimization, bi-objective optimization,
performance measures, performance assessment

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    .. grid-item-card:: API Reference
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        :link: reference
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        The reference guide contains a detailed description of the functions,
        modules, and objects.

    .. grid-item-card:: Examples
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        :link: auto_examples
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        Detailed examples and tutorials.

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