Intended Audience
- Science/Research
Operating System
- OS Independent
Programming Language
- Python :: 3 :: Only
- Python :: 3.10
- Python :: 3.11
- Python :: 3.12
- Python :: 3.13
- Python :: 3.14
moocore: Core Algorithms for Multi-Objective Optimization
[ Homepage ] [ GitHub ] [ ClickPy Dashboard ]
Contributors: Manuel López-Ibáñez, Fergus Rooney.
Introduction
The goal of moocore is to collect and document fast implementations of core mathematical functions and algorithms for multi-objective optimization. These functions include:
- Generate and transform nondominated sets.
- Identify and filter 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
For more details, see the complete Documentation.
Install
You can install the latest release using pip, for example:
python3 -m pip install moocore
Or to build the latest development version from GitHub:
python3 -m pip install 'git+https://github.com/multi-objective/moocore.git#egg=moocore&subdirectory=python'
Building the development version requires a C/C++ compiler. Instead, you can install pre-compiled development wheels for your operating system. See the list of wheels here (https://github.com/multi-objective/moocore/tree/wheels), click in the wheel you wish to install then copy the View Raw link. For example,
python3 -m pip install https://github.com/multi-objective/moocore/raw/refs/heads/wheels/moocore-0.1.5.dev0-py3-none-macosx_10_9_universal2.whl
If the URL does not have the word raw then you are not using the View Raw link.
R package
There is also a moocore package for R: https://multi-objective.github.io/moocore/r
