How to Build a Genetic Algorithm from Scratch in Python

A complete walkthrough on how one can build a Genetic Algorithm from scratch in Python, along with a real-world use case

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Introduction

The beauty of Genetic Algorithms is that they are directly inspired by nature, more specifically, the process of natural selection:

Natural selection, process that results in the adaptation of an organism to its environment by means of selectively reproducing changes in its genotype, or genetic constitution [1].

More broadly speaking, natural selection is one of the primary mechanisms of evolution. I am fascinated by this mechanism, which has changed the natural world over millions of years. On an even more exciting note, we can leverage the power of natural selection via Genetic Algorithms.

A Genetic Algorithm simulates natural selection in a non-natural environment, typically resembling a business resource optimization. Still, it is certainly not limited to these types of use cases.

In this article, I will show the reader how to build their own Genetic Algorithm with Python and apply it to a real-world use case.

Why use a Genetic Algorithm?