Procedural Fragment Shader Generation Using Classic Machine Learning

As mentioned in [A Tool for the Procedural Generation of Shaders using Interactive Evolutionary Algorithms
](https://arxiv.org/abs/2312.17587) paper, this project is divided into two major steps:

1. Implement the visual shader graph functionalities.
2. Apply a proper Genetic Algorithm to find the best shader for a pre-specified image.

The contributor may also read [Noise Modeler: An Interactive Editor and Library for Procedural Terrains via Continuous Generation and Compilation of GPU Shaders](https://link.springer.com/chapter/10.1007/978-3-319-24589-8_42) paper for a better understanding of the graph editor architecture.

Mentors: Robert, Josh, Greg
Difficulty: Medium - Hard
Expected size: 350h
Skills required: C++ fundamentals, Classic Machine Learning, Qt
Skills preferred: Knowledge of Genetic Programming is preferred as well as Deep Learning theory