Implementing a Genetic Algorithm from scratch in Python.Genetic Algorithms are a family of optimisation techniques that loosely resemble evolutionary processes in nature. It may be a crude analogy, but if you squint your eyes, Darwin’s Natural Selection does roughly resemble an optimisation task where the aim is to craft organisms perfectly suited to thrive in their environments.
Genetic Algorithms are a family of optimisation techniques that loosely resemble evolutionary processes in nature. It may be a crude analogy, but if you squint your eyes, Darwin’s Natural Selection does roughly resemble an optimisation task where the aim is to craft organisms perfectly suited to thrive in their environments. While it may have taken many millennia for humans to develop opposable thumbs and eagles to develop 20/4 vision, in this article I will show how to implement a Genetic Algorithm in Python for “evolving” a rubbish collecting robot in a couple of hours.
The best example I’ve come across to demonstrate how Genetic Algorithms work comes from a fantastic book on complex systems by Melanie Mitchell called “Complexity: A Guided Tour” (highly recommended). In one chapter, Mitchell introduces a robot named Robby whose sole purpose in life is to pick up rubbish and describes how to optimise a control strategy for Robby using a GA. Below I will explain my approach to solving this problem and show how to implement the algorithm in Python. There are some great packages for constructing these kinds of algorithms (such as DEAP) but in this tutorial, I will only be using base Python, Numpy and TQDM (optional).
While this is only a toy example, GAs are used in a number of real-world applications. As a data scientist, I most often use them for hyper-parameter optimisation and model choice. While they can be computationally expensive, GAs allow us to explore multiple areas of a search space in parallel and are a good option when computing a gradient is difficult.
A robot named Robby lives in a two-dimensional grid world full of rubbish and surrounded by 4 walls (shown below). The aim of this project is to evolve an optimal control strategy for Robby that will allow him to pick up rubbish efficiently and not crash into walls.
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