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Genetic Algorithms Workshop Cover

🧬 Genetic Algorithms Workshop

by MadBrain Labs

Learn how evolution inspires algorithms — from natural selection to optimization with Python.

📘 Overview

Welcome to the Genetic Algorithms Workshop — a hands-on coding series where you’ll build, visualize, and understand evolutionary algorithms step by step using Python and the DEAP framework.

This repository contains:

  • 🧩 Interactive exercises (.ipynb) with guided ### TODO sections.
  • ✅ Full solutions for each problem.
  • 🖼️ The PDF presentation used during the workshop.
  • 🔗 Links to open each notebook directly in Google Colab.

📖 Read the full accompanying article here: 👉 Hands-On Genetic Algorithms with Python

🧠 What You’ll Learn

  • The intuition behind Genetic Algorithms (GAs) — inspired by Darwin’s theory of evolution.

  • How to represent, mutate, and evolve digital organisms.

  • Step-by-step GA workflow:

    1. Population initialization
    2. Fitness evaluation
    3. Selection, crossover, mutation
    4. Statistics and visualization

  • Applying GAs to solve problems like:

    • 🎯 OneMax Problem — evolve the perfect binary string.
    • 🎒 Knapsack Problem — maximize value under constraints.
    • 👩‍⚕️ Nurse Scheduling Problem — handle hard and soft constraints.

📂 Repository Structure

Genetic-Algorithms-Workshop/
│
├── 📄 README.md                 ← You are here
├── 🧾 slides.pdf                ← Full presentation
│
├── notebooks/
│   ├── 01_one_max_exercise.ipynb
│   ├── 01_one_max_solution.ipynb
│   ├── 02_knapsack_exercise.ipynb
│   ├── 02_knapsack_solution.ipynb
│   ├── 03_nurse_scheduling_exercise.ipynb
│   ├── 03_nurse_scheduling_solution.ipynb

🚀 Run the Workshop in Google Colab

You can open each exercise directly in Colab — no installation required!

Notebook Exercise Solution
🧩 OneMax Open in Colab View Solution
🎒 Knapsack Open in Colab View Solution
👩‍⚕️ Nurse Scheduling Open in Colab View Solution

🧰 Requirements

To run locally:

pip install deap matplotlib seaborn numpy jupyter

Then launch Jupyter Notebook:

jupyter notebook notebooks/

Start with:

01_one_max_exercise.ipynb

🖼️ Presentation

📑 Download the PDF slides: ➡️ slides.pdf

The slides cover:

  • The story of Darwin and the finches 🐦
  • How evolution translates into computation
  • Key GA components (population, fitness, mutation)
  • Demo visuals and diagrams

🎓 Workshop Outline

Part Topic Description
🧩 1 OneMax Problem Introduction to genetic representation and basic operators.
🎒 2 Knapsack Problem Combining value optimization with constraints.
👩‍⚕️ 3 Nurse Scheduling Handling hard and soft constraints using GA.

🧠 Read More

Article: Hands-On Genetic Algorithms with Python Learn the theory behind this workshop — how nature’s principles can guide machine intelligence.

🤝 Contributing

Contributions and improvements are welcome! Feel free to open an issue or submit a pull request.

🧬 About MadBrain Labs

MadBrain Labs is building brain-inspired AI for humanoid robots — merging neuroscience, robotics, and machine learning. Learn more at madbrain.ai