I began this project as a form of hands-on revision for my university computational intelligence course, where I would be able to practice implementing a series of population-based optimisation algorithms.

I decided on using python, as the course was doing the same and it is a language I am veyr familiar with. I started off simply implementing each algorithm from pseudo-code and testing them by printing their outputs to the console based on how they solved basic mathematical fitness functions for finding global optima.

Afterwards, I searched for a GUI library I could use to give the algorithm some visual aid so I could learn a different abstraction of the decision space and how the algorithms worked differently.

I was able to study a more visual version of how the algorithms solved problems without the need for gradient descent, and also learned to implement them in an object-oriented way with interchangable fitness functions and algorithms through the GUI library that I found.