Optimisation modules built for optimization problem during thesis
Jérôme BUISINE
d836065f75
documentation of validator added
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| .github | 3 anni fa | |
| docs | 3 anni fa | |
| examples | 3 anni fa | |
| macop | 3 anni fa | |
| .gitignore | 3 anni fa | |
| CONTRIBUTING.md | 4 anni fa | |
| LICENSE | 3 anni fa | |
| README.md | 4 anni fa | |
| __init__.py | 5 anni fa | |
| build.sh | 4 anni fa | |
| logo_macop.png | 4 anni fa | |
| paper.bib | 4 anni fa | |
| paper.md | 4 anni fa | |
| requirements.txt | 4 anni fa | |
| setup.py | 3 anni fa |
README.md
Minimalist And Customisable Optimisation Package
Description
macop is an optimisation Python package which not implement the whole available algorithms in the literature but let you the possibility to quickly develop and test your own algorithm and strategies. The main objective of this package is to be the most flexible as possible and hence, to offer a maximum of implementation possibilities.
Modules
- algorithms: generic and implemented OR algorithms
- evaluator: example of an evaluation function to use (you have to implement your own evaluation function)
- solutions: solutions used to represent problem data
- operators: mutators, crossovers update of solution. This folder also has
policiesfolder to manage the way of update and use solution. - callbacks: callbacks folder where
Callbackclass is available for making callback instructions every number of evaluations.
Note: you can pass a custom validator function to the algorithm in order to check if a solution is always correct for your needs after an update.
How to use ?
Fully documentation of package with examples is available.
You can also see examples of use:
- in the knapsackExample.py python file for mono-objective.
- in the knapsackMultiExample.py python file for multi-objective.
Add as dependency
git submodule add https://github.com/jbuisine/macop.git