|Jérôme BUISINE 2b1151e24b update of references into paper and figure||3 months ago|
|.github||3 months ago|
|docs||3 months ago|
|examples||3 months ago|
|macop||3 months ago|
|.gitignore||3 months ago|
|CONTRIBUTING.md||3 months ago|
|LICENSE||3 months ago|
|README.md||3 months ago|
|__init__.py||1 year ago|
|build.sh||4 months ago|
|paper.bib||3 months ago|
|paper.md||3 months ago|
|requirements.txt||8 months ago|
|setup.py||3 months ago|
Macop is a python package for solving discrete optimisation problems in nature. Continuous optimisation can also applicable if needed. The objective is to allow a user to exploit the basic structure proposed by this package to solve a problem specific to him. The interest is that he can quickly abstract himself from the complications related to the way of evaluating, comparing, saving the progress of the search for good solutions but rather concentrate if necessary on his own algorithm. Indeed,
Macop offers the following main and basic features:
validatoris function which is used for validate or not a solution data state ;
computemethod in order to evaluate a solution ;
Based on all of these generic and/or implemented functionalities, the user will be able to quickly develop a solution to his problem while retaining the possibility of remaining in control of his development by overloading existing functionalities if necessary.
Main idea about this Python package is that it does not which doesn't implement every algorithm in the literature but let the possibility to the user to quickly develop and test its own algorithms and strategies. The main objective of this package is to provide maximum flexibility, which allows for easy experimentation in implementation..
Fully documentation of package with examples is available.
You can also see examples of use:
git submodule add https://github.com/jbuisine/macop.git