PYGENALGO: A simple and powerful toolkit for genetic algorithms

Genetic algorithms (GAs) are meta-heuristic algorithms that are used for solving constrained and unconstrained optimization problems, mimicking the process of natural selection in biological evolution. Due to the fact that GAs do not require the optimization function to be differentiable, they are suitable for application in cases where the derivative of the objective function is either unavailable or impractical to obtain numerically. This paper proposes a general purpose genetic algorithm toolkit, implemented in Python3 programming language, having only minimum dependencies in NumPy and Joblib, that handle some of the numerical and parallel execution details.