About
This blog explores the intersection of machine learning, genetic algorithms, and automated discovery. The focus is on building systems that find solutions rather than engineering them by hand.
What You’ll Find Here
- Deep dives into optimization algorithms and their alternatives
- Investigations into evolutionary approaches to ML problems
- Analysis of what makes certain techniques work (or fail)
- Practical experiments with code and results
The Approach
Every post aims to be:
- Data-driven — Claims backed by experiments, not just theory
- Accessible — Technical content explained for broader audiences
- Honest — Including limitations, failures, and open questions