Version: 0.0.9999 (See What’s new)
Date Mar 18, 2025
Useful links: Source Repository | Issue Tracker
This webpage documents the mooplot Python package. There is also a mooplot R package.
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.
API Reference
The reference guide contains a detailed description of the functions, modules, and objects.
mooplot API ReferenceExamples
Detailed examples and tutorials.
ExamplesRetroSearch 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