[ Python package ] [ GitHub ] 
Contributors: Manuel López-Ibáñez, Carlos M. Fonseca, Luís Paquete, Mickaël Binois. Fergus Rooney.
The mooplot package implements various visualizations that are useful in multi-objective optimization. These visualizations include:
- Visualization of Pareto frontiers.
- Visualization of the Empirical Attainment Function (EAF) and the differences between EAFs. The EAF describes the probabilistic distribution of the outcomes obtained by a stochastic algorithm in the objective space.
These visualizations may be used for exploring the performance of stochastic local search algorithms for multi-objective optimization problems and help in identifying certain algorithmic behaviors in a graphical way.
Keywords: empirical attainment function, summary attainment surfaces, EAF differences, multi-objective optimization, graphical analysis, visualization.
The repository is composed of:
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.