**moocore**: Core Algorithms for Multi-Objective Optimization
=============================================================
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[ [**Homepage**][py-moocore-homepage] ]
[ [**GitHub**][py-moocore-github] ]
**Contributors:**
[Manuel López-Ibáñez](https://lopez-ibanez.eu),
Fergus Rooney.
---------------------------------------
Introduction
============
The goal of **moocore** is to collect fast implementations of core mathematical functions and algorithms for multi-objective optimization. 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.
**Keywords**: empirical attainment function, summary attainment surfaces, EAF
differences, multi-objective optimization, bi-objective optimization,
performance measures, performance assessment
R package
---------
There is also a `moocore` package for R: https://multi-objective.github.io/moocore/r
[py-build-badge]: https://github.com/multi-objective/moocore/actions/workflows/python.yml/badge.svg?event=push
[py-build-link]: https://github.com/multi-objective/moocore/actions/workflows/python.yml
[py-coverage-badge]: https://codecov.io/gh/multi-objective/moocore/branch/main/graph/badge.svg?flag=python
[py-coverage-link]: https://app.codecov.io/gh/multi-objective/moocore/tree/main/python
[py-moocore-github]: https://github.com/multi-objective/moocore/tree/main/python#readme
[py-moocore-homepage]: https://multi-objective.github.io/moocore/python
[py-moocore-pypi]: https://pypi.org/project/moocore/
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