This project is the minor task submitted to Dr. Alaa Hamdy as part of the Selected Topics in Data Science Course: Faculty of Engineering ICHEP, Ain Shams University.
The Genetic Algorithm for Block Optimization is designed to enhance data compression efficiency by optimizing the block width and height parameters within the Constant Area Coding (CAC) framework. Data compression plays a critical role in reducing storage requirements and minimizing data transmission costs. Traditional methods often rely on fixed or heuristically determined block parameters, which may not yield optimal compression ratios. This project leverages the power of genetic algorithms—a nature-inspired optimization technique—to dynamically find the optimal block dimensions that maximize the lossless compression ratio.
- To run the project:
pip install -r requirements.txt
- Run all cac.ipynb cells.