Optimisation modules built for optimization problem during thesis

Jérôme BUISINE 6313a6a393 enable n objectives for MOEAD		vor 4 Jahren
.github	df8251b5ac new package name : macop; use of new policy for operators	vor 4 Jahren
docs	9bd549721d add pareto checkpoint; update moead use	vor 4 Jahren
macop	6313a6a393 enable n objectives for MOEAD	vor 4 Jahren
.gitignore	9bd549721d add pareto checkpoint; update moead use	vor 4 Jahren
CONTRIBUTING.md	df8251b5ac new package name : macop; use of new policy for operators	vor 4 Jahren
LICENSE	3589fb944b Initial commit	vor 5 Jahren
README.md	7aaf6b1d90 now use of callbacks list into algorithm	vor 4 Jahren
__init__.py	0a1b108095 First version of OR framework	vor 5 Jahren
build.sh	df8251b5ac new package name : macop; use of new policy for operators	vor 4 Jahren
knapsackExample.py	f358eca024 start of MOEAD implementation	vor 4 Jahren
knapsackMultiExample.py	6313a6a393 enable n objectives for MOEAD	vor 4 Jahren
logo_macop.png	df8251b5ac new package name : macop; use of new policy for operators	vor 4 Jahren
requirements.txt	4fbbbf39d3 documentation updates for algorithms	vor 4 Jahren
setup.py	7aaf6b1d90 now use of callbacks list into algorithm	vor 4 Jahren

Minimalist And Customizable Optimization Package

Description

macop is an optimization 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 policies folder to manage the way of update and use solution.
callbacks: callbacks folder where Callback class 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 is solution is always correct for your needs after an update.

How to use ?

You can see an example of use in the knapsackExample.py python file.

Fully documentation of package with examples is also available.

Add as dependency

git submodule add https://github.com/jbuisine/macop.git

License

The MIT License

README.md