https://github.com/arduino88/evolutionary-dots

A simple pygame genetic-algorithm project simulating evolution of dots to find a path around obstacles to a goal.
https://github.com/arduino88/evolutionary-dots

evolutionary-algorithm genetic-algorithm machine-learning mutation numpy pathfinding pygame python3

Last synced: 11 days ago
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A simple pygame genetic-algorithm project simulating evolution of dots to find a path around obstacles to a goal.

• Host: GitHub
• URL: https://github.com/arduino88/evolutionary-dots
• Owner: Arduino88
• Created: 2024-05-11T02:02:02.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-05-13T19:30:07.000Z (9 months ago)
• Last Synced: 2024-12-04T12:11:55.285Z (2 months ago)
• Topics: evolutionary-algorithm, genetic-algorithm, machine-learning, mutation, numpy, pathfinding, pygame, python3
• Language: Python
• Homepage:
• Size: 1.99 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
## Evolutionary Dots

A personal project inspired by the one and only **[Code Bullet](https://www.youtube.com/@CodeBullet)**

**This is my first foray into the world of numpy-only genetic algorithms**

#### Overview

In this program, a group of dots are released into an environment with walls and a goal object.

The dots have simulated velocity and acceleration, as well as an input list of acceleration vectors, used as "genes", subject to mutations and natural selection.

![](https://github.com/Arduino88/Evolutionary-Dots/blob/main/evolutionary-dots.gif?raw=true)

#### In each run:

- The dots iterate over their respective list, setting their acceleration to the contained vector, and checking for collisions.

- Once every dot has either died or reached the goal, their fitnesses are calculated:

  - fitness = 1 / (distance to goal) ^2.

  - if touching goal, fitness is multiplied by the remaining steps in the list (incentivizes more efficient paths).

  - if dead (collided with wall or screen border), divide fitness by two.

- Then, a new set of dots are created, with a probability of having a dot as a parent proportional to the parent's fitness (fitter dots are more likely as parents).

- Randomly, for each child in the new dot set, A gaussian noise distribution is calculated and applied to existing vectors.

- This is controlled by a mutation rate variable, with a low probability of an individual vector being mutated.

- The best dot is saved from the previous run, and is added to the next generation to prevent detrimental mutations regressing progress (This is the green dot).

- All dots are re-initialized at the beginning of their acceleration list, and placed at the shared starting position.

- The next run is initiated.