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Optimisation modules built for optimization problem during thesis

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Jérôme BUISINE 1892c4e38d MO development for knapsack il y a 4 ans
.github df8251b5ac new package name : macop; use of new policy for operators il y a 4 ans
docs f358eca024 start of MOEAD implementation il y a 4 ans
macop 1892c4e38d MO development for knapsack il y a 4 ans
.gitignore 2e68489694 update whole class documentation; add algo reference into policy il y a 4 ans
CONTRIBUTING.md df8251b5ac new package name : macop; use of new policy for operators il y a 4 ans
LICENSE 3589fb944b Initial commit il y a 5 ans
README.md 7aaf6b1d90 now use of callbacks list into algorithm il y a 4 ans
__init__.py 0a1b108095 First version of OR framework il y a 5 ans
build.sh df8251b5ac new package name : macop; use of new policy for operators il y a 4 ans
knapsackExample.py f358eca024 start of MOEAD implementation il y a 4 ans
knapsackMultiExample.py 1892c4e38d MO development for knapsack il y a 4 ans
logo_macop.png df8251b5ac new package name : macop; use of new policy for operators il y a 4 ans
requirements.txt 4fbbbf39d3 documentation updates for algorithms il y a 4 ans
setup.py 7aaf6b1d90 now use of callbacks list into algorithm il y a 4 ans

README.md

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