Platypus-Parallel

What's new?

• Solution evaluation is parallelized using joblib library.
• Changes work as a drop-in replacement for existing platypus-opt project.

What is Platypus?

Platypus is a framework for evolutionary computing in Python with a focus on multiobjective evolutionary algorithms (MOEAs). It differs from existing optimization libraries, including PyGMO, Inspyred, DEAP, and Scipy, by providing optimization algorithms and analysis tools for multiobjective optimization. It currently supports NSGA-II, NSGA-III, MOEA/D, IBEA, Epsilon-MOEA, SPEA2, GDE3, OMOPSO, SMPSO, and Epsilon-NSGA-II. For more information, see our IPython Notebook or our online documentation.

Speed-up

• Speed-up is build into testing: there's no purpose in parallization if it has no tangible benefit.
• Due to multithreading overhead (setting up threads, sharing resources, etc.), simple evaluations will perform faster using vanilla Python.
• For examples of ideally expensive problems, see the /examples folder.

Installation

To install the latest development version of Platypus, run the following commands:

    git clone https://github.com/Cutwell/platypusparallel.git
    cd platypusparallel
    python setup.py install

import platypusparallel

License

Platypus is released under the GNU General Public License.