Learning from ants: Ant colony optimization algorithms are versatile and useful for several real-world applications. These applications usually center on complex optimization problems. Here are three uses for the algorithm.

If you haven’t already, gain the intuition of the ant colony optimization algorithm here: https://rhurbans.com/ant-colony-optimization-for-beginners/

Route optimization

In a logistics example, perhaps…


A single ant can carry 10 to 50 times its own body weight and run 700 times its body length per minute. These are impressive qualities; however, when acting in a group, that single ant can accomplish much more.

In groups, ants build colonies, retrieve food, and even use peer…


Genetic algorithms are a fascinating technique for solving optimisation problems. If you can create a set of rules that can measure a solution’s performance, you can probably use a GA to help solve the problem. Here are some ways to encode solutions.

If you missed my article on the intuition…


Genetic algorithms are part of the family of optimization algorithms. They operate on the theory of evolution, more particularly, genetic evolution. Each solution is a chromosome that’s made up of genes, and is evaluated to determine how well it performs. This repeats until a good solution is found.

Evolution suggests…


Imagine how a swarm of bees find food sources. While visiting areas, different bees will find plants of different quality and quantity. Some might be better than others but they gravitate towards the best. Optimisation algorithms in AI work this way too.

Optimisation algorithms are used to evaluate massive search…


When we look at the world around us, we sometimes wonder how everything we see and interact with came to be. One way to explain this is the theory of evolution. And it’s useful in solving computational problems in AI.

The theory of evolution suggests that the living organisms that…


Do you know how IBM’s Deep Blue chess computer controversially beat champion, Gary Kasparov in 1997? It’s a search algorithm called min-max. This article describes how it works at a high-level.

Adversarial search is characterized by opposition or conflict. These problems require us to anticipate, understand, and counteract the actions…


When you’re deciding if you’d try a specific pizza, you may have some criteria that it passes. The pizza might be made by someone different with a different technique, but as long as it passes your set of rules, you’ll try it. This is a heuristic.

Often described as a…


Remember our search algorithm trip to the beach? If not check out this article: https://rhurbans.com/plan-search-repeat. Our trip can be represented as a graph. What’s a graph? It’s a data structure used by algorithms to do smart things.


Suppose we’re going on a trip to the beach. It’s 500 km away, with two stops: one at a petting zoo and one at a pizza restaurant. We will sleep at a lodge close to the beach on arrival and partake in three activities. …

Rishal Hurbans

Author of Grokking AI Algorithms • Building at Prolific Idea and Viszen • Business solutions at Entelect

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