MH-Builder is a C++ framework for designing adaptive metaheuristics for single and multi-objective optimization that is developed by ORKAD team. Its particularity is to be able to modify during the execution the components of your metaheuristic.
Tutorial on: https://mh-builder-orkad.univ-lille.fr/
Code source on: https://gitlab.cristal.univ-lille.fr/orkad-public/mh-builder
MOCA-I is a Pittsburgh classification rule-mining system using partial classification rules particularly well adapted to imbalanced data. It is based on a multi-objective local search using Confidence, Sensitivity and rule length to explore the candidate.
As MOCA-I software is embedded in the Gitlab project "mh-builder.git", the installation process is identical to that of the mh-builder framework.
Documentation is available at: https://mh-builder-orkad.univ-lille.fr/
Mary-Morstan is a multi objective modular framework to automatically configure machine learning algorithms (AutoML). This python automated machine learning tool is based on evolutionary algorithms.
Mary-Morstan is modular in such a way that the exploration versus exploitation process can be tuned through the specification of an Evolutionary Algorithm (EA) space. It also allows to deal with big data files and various of classification and regression problems.
Documention is available on: https://mh-builder-orkad.univ-lille.fr/
Source code is available on: https://gitlab.cristal.univ-lille.fr/orkad-public/mary-morstan