  In recent years, optimization problems have become increasingly more
prevalent due to the need for more powerful computational methods. With the
more recent advent of technology such as artificial intelligence, new
metaheuristics are needed that enhance the capabilities of classical
algorithms. More recently, researchers have been looking at Charles Darwin's
theory of natural selection and evolution as a means of enhancing current
approaches using machine learning. In 1960, the first genetic algorithm was
developed by John H. Holland and his student. We explore the mathematical
intuition of the genetic algorithm in developing systems capable of evolving
using Gaussian mutation, as well as its implications in solving optimization
problems.
