Optimisation modules built for optimization problem during thesis

3 Atzari

Jérôme BUISINE 63b1556f99 available all operators modules		3 gadi atpakaļ
.github	df8251b5ac new package name : macop; use of new policy for operators	3 gadi atpakaļ
docs	63b1556f99 available all operators modules	3 gadi atpakaļ
macop	d2636ebd1f Update text examples	3 gadi atpakaļ
.gitignore	9bd549721d add pareto checkpoint; update moead use	3 gadi atpakaļ
CONTRIBUTING.md	df8251b5ac new package name : macop; use of new policy for operators	3 gadi atpakaļ
LICENSE	3589fb944b Initial commit	4 gadi atpakaļ
README.md	7aaf6b1d90 now use of callbacks list into algorithm	3 gadi atpakaļ
__init__.py	0a1b108095 First version of OR framework	4 gadi atpakaļ
build.sh	df8251b5ac new package name : macop; use of new policy for operators	3 gadi atpakaļ
knapsackExample.py	f358eca024 start of MOEAD implementation	3 gadi atpakaļ
knapsackMultiExample.py	b38a85f3b0 update ucb policy using EvE with exploration rate	3 gadi atpakaļ
logo_macop.png	df8251b5ac new package name : macop; use of new policy for operators	3 gadi atpakaļ
requirements.txt	4fbbbf39d3 documentation updates for algorithms	3 gadi atpakaļ
setup.py	63b1556f99 available all operators modules	3 gadi atpakaļ

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