stprogrammer / ga-simulation-simple

Demonstration of Genetic Algorithm with simple simulation in Python

Genetic algorithms simulation, simple

Answer to assignment from "AI methods and applications" course, DTE-2501 autumn 2021, from UiT The Arctic University of Norway.

The starter code consists of 3 python files: GAengine.py, Main.py and Utils.py. You will need to install pygame in your environment for the code to work. Main.py contains the render code. The initial population is also initialized here.

GAengine.py will contain the bulk of your genetic algorithm implementation. There are skeleton functions in place for fitness assignment and selection. Utils.py contains helper classes. GAPoint is a simple class representing a position in the world, with a function to find the distance to another GAPoint. Chromosome represents the chromosome and has skeleton functions for crossover and mutation. The chromosome only has two features, the x and y position of the individual in the world, used for rendering the individual in the simulator. They are already implemented using high-level representation in the class GAPoint.