ホーム エクスプローラ ヘルプ
サインイン
Prise3D
/
Thesis-OptimizationModules
5
0
フォーク 0
ファイル 課題 0 プルリクエスト 0 Wiki

Optimisation modules built for optimization problem during thesis

ツリー: f7614a0aea
ブランチ タグ
Thesis-Optim...
ZIP TAR.GZ
Jérôme BUISINE f7614a0aea MOEAD examples and tutorial added 4 年 前
.github df8251b5ac new package name : macop; use of new policy for operators 4 年 前
docs f7614a0aea MOEAD examples and tutorial added 4 年 前
macop f7614a0aea MOEAD examples and tutorial added 4 年 前
.gitignore 9bd549721d add pareto checkpoint; update moead use 4 年 前
CONTRIBUTING.md df8251b5ac new package name : macop; use of new policy for operators 4 年 前
LICENSE 3589fb944b Initial commit 5 年 前
README.md 7aaf6b1d90 now use of callbacks list into algorithm 4 年 前
__init__.py 0a1b108095 First version of OR framework 5 年 前
build.sh df8251b5ac new package name : macop; use of new policy for operators 4 年 前
knapsackExample.py f358eca024 start of MOEAD implementation 4 年 前
knapsackMultiExample.py 6313a6a393 enable n objectives for MOEAD 4 年 前
logo_macop.png df8251b5ac new package name : macop; use of new policy for operators 4 年 前
requirements.txt 4fbbbf39d3 documentation updates for algorithms 4 年 前
setup.py f7614a0aea MOEAD examples and tutorial added 4 年 前

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