Metadata-Version: 1.2
Name: pymoo
Version: 0.2.4
Summary: Multi-Objective Optimization Algorithms
Home-page: https://github.com/msu-coinlab/pymoo
Author: Julian Blank
Author-email: blankjul@egr.msu.edu
License: Apache License 2.0
Description: pymoo - Multi-Objective Optimization Framework
        ====================================================================
        
        You can find the detailed documentation here:
        https://www.egr.msu.edu/coinlab/blankjul/pymoo/
        
        
        .. image:: https://gitlab.msu.edu/blankjul/pymoo/badges/master/pipeline.svg
           :alt: pipeline status
           :target: https://gitlab.msu.edu/blankjul/pymoo/commits/master
        
        
        Installation
        ====================================================================
        
        First, make sure you have a python environment installed. We recommend miniconda3 or anaconda3.
        
        .. code:: bash
        
            conda --version
        
        Then from scratch create a virtual environment for pymoo:
        
        .. code:: bash
        
            conda create -n pymoo -y python==3.6 cython numpy
            conda activate pymoo
        
        
        For the current stable release please execute:
        
        .. code:: bash
        
            pip install pymoo
        
        For the current development version:
        
        .. code:: bash
        
            git clone https://github.com/msu-coinlab/pymoo
            cd pymoo
            pip install .
        
        Since for speedup some of the modules are also available compiled you can double check
        if the compilation worked. When executing the command be sure not already being in the local pymoo
        directory because otherwise not the in site-packages installed version will be used.
        
        .. code:: bash
        
            python -c "from pymoo.cython.function_loader import is_compiled;print('Compiled Extensions: ', is_compiled())"
        
        
        
        Usage
        ==================================
        
        We refer here to our documentation for all the details.
        However, for instance executing NSGA2:
        
        .. code:: python
        
            
            from pymoo.optimize import minimize
            from pymoo.util import plotting
            from pymop.factory import get_problem
        
            # create the optimization problem
            problem = get_problem("zdt1")
            pf = problem.pareto_front()
        
            res = minimize(problem,
                           method='nsga2',
                           method_args={'pop_size': 100},
                           termination=('n_gen', 200),
                           pf=pf,
                           save_history=False,
                           disp=True)
            plotting.plot(pf, res.F, labels=["Pareto-front", "F"])
        
        
        
        Contact
        ====================================================================
        Feel free to contact me if you have any question:
        
        | Julian Blank (blankjul [at] egr.msu.edu)
        | Michigan State University
        | Computational Optimization and Innovation Laboratory (COIN)
        | East Lansing, MI 48824, USA
        
        
Keywords: optimization
Platform: any
Requires-Python: >3.3
