Genetic Algorithms is a field of computer science that have many applications, ranging from teaching robots how to overcome a problem, to improving designs and optimizing solutions. Genetic Algorithms is a perfect tool for optimization of an AI’s thought process. In this thesis, I explore one way of how to make an AI-bot move through an area with obstacles and try to get to a goal somewhere on that playing field. Genetic Algorithms will be used to improve how the AI reacts when faced with obstacles and getting better at it with each generation. The thesis shows that it is possible to use Genetic Algorithms to optimize an AI at a specific task and a notion that the more resistance it encounters, the the better it gets.

Arbetet handlar om hur man kan använda Genetiska Algoritmer för att lära en AI att röra sig igenom ett område med hinder för att hitta ett mål.