evopy 0.3

Evolutionary Strategies made simple

evopy

Evolutionary Strategies made simple!

Use evopy to easily optimize a vector of floats in Python.

🏗 Installation

All you need to use evopy is Python 3! Run this command to fetch evopy from PyPI:

pip install evopy

Then you can import EvoPy like this:

from evopy import EvoPy

⏩ Usage

One-Dimensional Example

Let's say we wanted to find the optimum of a parabola, without using exact methods from calculus! With Evopy, this is as easy as writing the following two lines:

evopy = EvoPy(lambda x: pow(x, 2), 1)
best_coordinates = evopy.run()

The main ingredient here is the fitness function (the lambda). This can also be a normal function reference, just make sure that it accepts a float or an array of floats and outputs a single float. The other ingredient is the 1 at the end of the first line: This is the dimensionality of the inputs that you expect in your fitness function. best_coordinates will contain an array with a single element, which is the best x value the algorithm could find in the default number of generations.

Multi-Dimensional Example

If the previous example seemed too simple to you, we can also look at the optimum of a more complex, two-dimensional function, like the Rastrigin function. We don't have to modify much in our previous code snippet to get this to work:

evopy = EvoPy(
    lambda X: 5 + sum([(x**2 - 5 * np.cos(2 * np.pi * x)) for x in X]), 
    2, 
    generations=1000, 
    population_size=100
)
best_coordinates = evopy.run()

Compared to the first example, we have interchanged the fitness function for a more complex one, set the dimensionality to 2, and given the algorithm more time to find an optimum by setting a higher generation and individual count than the default.

Docs

For more detailed information on evopy's functionality, have a look at the docs!

⛏ Development

Clone this repository and fetch all dependencies from within the cloned directory:

pip install -r requirements.dev.txt

Run all tests with:

nosetests

To check your code style, run:

pylint evopy

To measure your code coverage, run:

nosetests --with-coverage --cover-package=evopy --cover-html --cover-branches --cover-erase