# Fracnetics

**Fracnetics** is a high-performance **C++ library with Python bindings** for **Genetic Network Programming (GNP)** — an evolutionary approach that evolves **directed graph structures** for decision-making and control tasks.

> Bridging evolution, networks, and fractals into one framework.

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## Connect

[![LinkedIn](https://img.shields.io/badge/LinkedIn-Profile-blue?logo=linkedin&logoColor=white)](https://www.linkedin.com/in/fabian-koehnke/)


## 🔬 Overview

**Fracnetics** extends **Genetic Network Programming (GNP)** with **variable-size** networks and **fractal geometry**, designed to model adaptive, recursive, and self-similar network structures inspired by biological evolution and natural growth patterns.

It is designed for researchers and developers exploring:

- Evolutionary network design
- Training agents in dynamic environments
- Directed multigraph network architecture
- variable-size networks
- Complex systems with self-similar topology

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## 🚀 Installation

```bash
pip install fracnetics
```

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## 📓 Colab Tutorials

Small Tutorials Using Fracnetics

This notebooks demonstrate how to use the Fracnetics library to solve the CartPole environment problem from Gymnasium (a fork of OpenAI Gym) and the simple Irisdata classification.

**CartPole Tutorial**  
[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](
https://colab.research.google.com/github/FabianKoehnke/fracnetics/blob/main/notebooks/minExampleCartPole.ipynb)

**Iris Tutorial**  
[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](
https://colab.research.google.com/github/FabianKoehnke/fracnetics/blob/main/notebooks/minExampleIRIS.ipynb)
 
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## 🦾 Features

- **Population Management**  
  - Create and manage a full population of `Network` individuals.  
  - Graph-Based Evolution: Networks evolve as directed graphs with judgment nodes (conditional decisions) and processing nodes (actions), enabling node reuse and cyclic execution paths

- **Fitness Evaluation**  
    - Built-in support for OpenAI Gymnasium environments
    - Traverse a network path via ``callTraversePath`` and calculate any own fitness/ target function

- **Selection & Elitism**: Tournament selection with configurable size.

- **Mutation Operators**  
  - **Edge mutation** (altering connections between nodes).  
  - **Boundary mutations** (multiple variants):  
    - Uniform  
    - Normally distributed  
    - Network-size dependent sigma scaling  
    - Edge-count dependent sigma scaling  
    - Fractal-based mutation  

- **Crossover**: Node exchange between individuals.

- **Innovative Opreators**  
    - **Add/Delete Nodes** – dynamic structural changes in networks.
    - **Fractal Geometry Integration** – hierarchical boundary generation via production rules (L-systems-style subdivision).

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