Introduction to Cartesian Genetic Programming (CGP) - GECCO 2025 Tutorial

This repository contains the tutorial materials for the Cartesian Genetic Programming (CGP) tutorial presented at GECCO 2025. The tutorial provides a hands-on introduction to CGP through a simple, educational implementation built from scratch.

📖 About This Tutorial

Cartesian Genetic Programming is a form of genetic programming that represents programs as directed acyclic graphs. This tutorial covers:

• CGP representation: Linear genome encoding computational graphs
• Evolution: Simple (1+λ) evolutionary strategy
• Applications: Symbolic regression and reinforcement learning policy evolution
• Implementation: Complete CGP system built from scratch in Python

🎯 Learning Objectives

By the end of this tutorial, you will understand:

• How CGP represents programs as graphs with linear genomes
• The role of active and inactive nodes in CGP
• How to apply CGP to symbolic regression problems
• How to evolve policies for reinforcement learning tasks

📚 Tutorial Structure

The main tutorial is contained in the Jupyter notebook:

• cgp_tutorial.ipynb - Complete tutorial with theory and hands-on examples

Topics Covered:

1. Representation - CGP's graph-based program representation
2. Random Initialization - Creating random CGP programs
3. Active vs Inactive Nodes - Understanding program execution flow
4. Mutation - Point mutation and neutral evolution
5. Evolution - Simple (1+λ) evolutionary algorithm
6. Example 1: Symbolic regression (evolving f(x,y) = x² + y²)
7. Example 2: Reinforcement learning (Mountain Car policy evolution)

🚀 Getting Started

Local Installation

1. Clone this repository
2. Install the required dependencies

pip install numpy matplotlib gymnasium[classic_control]

3. Open cgp_tutorial.ipynb in Jupyter Lab/Notebook
4. Follow along with the tutorial!

Google Colab

1. Open this notebook directly on Colab

⚠️ Important Note

This is a simplified educational implementation designed for learning purposes. For production use or advanced research, consider the implementations listed below.

🔗 Production CGP Implementations

Python

• pyCGP: Python implementation focused on symbolic regression and image analysis
• CGPython: Another Python CGP implementation
• cartesian: Lightweight Python CGP for symbolic regression
• cartesian_genetic_programming: Sklearn-compatible CGP with cross-validation and grid search
• hal-cgp: Extensible pure Python library supporting symbolic regression with export to NumPy, SymPy, and PyTorch

Julia

• CartesianGeneticProgramming.jl: Julia implementation based on Cambrian.jl framework
• MAGE.jl: Multimodal Adaptive Graph Evolution, a typed extension of CGP

C/C++

• cgp-plusplus: Modern C++ implementation with concurrency, checkpointing, and benchmarks
• CGP-Library: Cross-platform C library for integration into larger projects

Specialized Implementations

• dcgp: Differentiable CGP with derivative information for advanced optimization
• dcgp.js: JavaScript bindings for differential CGP (Node.js and web)
• CGPExperiments: Python framework for CGP experiments in image processing
• CGPNAS: CGP for multi-objective neural architecture search
• gpFlappyBird: CGP trained to play Flappy Bird

👥 Tutorial Authors

This tutorial was developed for the GECCO 2025 conference. For questions or feedback about the tutorial materials, please open an issue in this repository.

📄 License

This tutorial is provided for educational purposes. Please refer to individual implementation repositories for their specific licenses.

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Happy Learning! 🧬💻