Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://github.com/multi-objective/mooplot below:

multi-objective/mooplot: Visualizations for Multi-Objective Optimization

[ R package ] [ GitHub ]

[ 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.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4