| Homepage | Code | Build status | Coverage | Package | Downloads |
|---|---|---|---|---|---|
| C library | GitHub |  |
|||
| R package | GitHub |  |
 |
 |
|
| Python package | GitHub |  |
 |
 |
 |
Contributors: Manuel López-Ibáñez, Carlos M. Fonseca, Luís Paquete, Andreia P. Guerreiro Mickaël Binois. Leonardo C.T. Bezerra, Fergus Rooney.
The goal of the moocore project 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.
The repository is composed of:
c/: C library and command-line tools.r/: An R package that uses the C library.python/: A Python package that uses the C library.
Each component is documented in the README.md file found under each folder.
In addition to the tests within each component, there is a separate comprehensive testsuite that is run before each release.
The following projects currently use moocore: