Algorithms

Looking at the images above, we see an example of an image posterization filter that gives images a cartoon-like appearance, but behind the scenes, this filter is actually using a machine learning algorithm known as clustering.

Before exploring into how this process works and seeing how we can implement it in Python, let’s take a look at why we might want to do this in the first place.

Imagine you want to travel around Europe. You want to see the sights, and visit as many European capital cities as possible, but you’re on a budget; flights can get expensive, so you plan to travel by rail and bus. To make your funds stretch as far as possible, you need to plan a route that will take you to all of the cities on your list for the cheapest price, before heading home again.

This is known as the Travelling Salesman Problem, and it’s a classic puzzle in computer science, operations research, and graph theory. While the problem itself may seem fairly straightforward, finding a solution proves deceptively challenging. Worse still, if you’ve found a solution that you think is the best, there is no quick or easy way to verify that it is in fact the optimal route.

The Travelling Salesman Problem, TSP, describes a scenario where a salesman wishes to visit a number of cities, while taking the shortest possible route, before returning home to the start point. While it may appear simple, this problem not only has no known polynomial time solution, but there is also no time-efficient way to prove that a given solution is in fact optimal.

Instead, we often use heuristic algorithms to give an approximate solution that is sufficient for many practical uses. In this article, we will explore a different approach to generating a ‘good’ solution using a Genetic Algorithm. For a more in-depth discussion of the difficulties of the TSP, as well as a summary of some of the heuristic methods used to solve it, check out this article.

In the ever-expanding field of artificial intelligence, some of the most fascinating advancements come from mimicking the natural world. One such breakthrough is the Genetic Algorithm, a powerful machine learning technique inspired by Charles Darwin’s theory of natural selection. Just as organisms evolve over time, honing their adaptations to thrive in their environments, Genetic Algorithms evolve solutions to complex problems, becoming increasingly effective with each iteration.