Hyperparameter optimization is all about selecting the best hyperparameters like learning rate, number of layers, activation functions etc. for neural networks.

On the other hands, Genetic Algorithms ( GEs ) are Evolutionary Algorithms which work on the principle given by Charles Darwin, "Only the fittest individual survives". A population would contain a specific number of individuals ( NNs ), and the fittest one ( the one with the smallest loss ) will be evolved!

We have our project Coding Feed-forward Neural Networks in Kotlin ( GitHub , Medium ) and extending it further, we add GE based hyperparameter optimization to it, all in your favourite Kotlin.

Individuals who love linear algebra, matrics, partial derivatives and other weird stuff are warned. This repo has no math for you guys, but biology we learnt in our early classes