Evolutionary Implementation

This repository contains a collection of Evolutionary Algorithm implementations and example problems solved using these methods.
Some of the implementations are based on assignments and materials from the Evolutionary Computation course in the Master’s in Artificial Intelligence program.

Repository Structure

Each directory in this repository is a self-contained project, including:

• its own Python codebase
• a dedicated README explaining the theory, approach, and results
• plots / figures (if applicable)
• a brief report or problem description

You can explore each implementation directly through its folder.

Tech Stack

Most implementations use:

• Python 3
• NumPy — numerical computation
• Matplotlib — visualization and performance plots

If additional libraries are used, they will be explicitly listed inside each project’s README.

📚 List of Implementations

• Genetic Algorithm for Scheduling
• N Queens with GA
• Simple GA
• Logistic Regresion ES

🚀 Usage

Clone the repository:

git clone git@github.com:ParsaBordbar/evolutionary-implementation.git
cd evolutionary-implementation

Run them using uv (or python3):

uv run main.py