Open source genetic algorithm framework. openGA works on Python >= 3.7.

The main goals for this library are:

• to be convenient to adapt to optimization problems
• to have clean and easy to understand code base
• to have minimal 3-rd party dependencies

• demo problem Seach optimal x-y combination to get maximal response of objective function. Surface plot of object function:

• theoretical solution

  $$ z = 4.00 \text{, at } x = 0.25, y= 0.25 $$

• openGA solution

  $$ z = 3.99 \text{, at } x = 0.2393, y = 0.2411 $$

  notice: solution will change marginally between each run for random searching machenism.

• openGA searching process Evolve 15 generations with 20 individuals in each iteration.

Check the examples folder for extended usage demo.

openGA is available on:

• github: https://github.com/neal-nie/openGA/
• PyPi: https://pypi.org/project/openGA/

pip install openGA

https://openga.readthedocs.io/en/latest/

openGA uses the following libraries:

• numpy : for array operation
• pandas : for process record

Please have a look over the contributing guidelines.

• SOGA
• NSGA

• codacy for static check
• codecov for coverage
• read the docs
• pypi