This program is a demonstration of a genetic algorithm, with the use of “rockets” drawn as rectangles. Each “rocket” has its own dna telling them random directions to accelerate towards. Rockets effectively gain a higher (better) fitness the closer they are to the goal (green circle) and lose fitness (worse) if they try and go out of bounds or hit a boundary. In the scope of the genetic algorithm, these fitness scores are used to create the next “generation” of rocket which favors (not absolute) to combine dna of the top performing rockets. This, in turn, simulates evolution, with each proceding generation utilizing the top performing “genes” of their predecessors.

This particular genetic algorithm also includes “Elitism”. The highest performing rocket from each generation is also chosen to be a part of the next generation (represented by the light blue rocket). This ensures, that there is a “check point” of sorts - preventing complete deviation from the goal as a result of mutation and poor performing crossovers.

Github Repository

The Github repository can be found here

What were my goals with this project?

  • Implementation of a genetic algorithm
  • Efficient manipulation of a large quantity of objects

Project Images



  • Pygame


This is a project designed and inspired from this youtube video: by “The Coding Train”

While it does follow the original structure of the video, my version of the project offers a translation (from javascript to python), demonstration and understanding of geometry (lack of built in drawing/rotation functions), knowledge of OOP programming, as well as an understanding and implementation of a genetic algorithm.