Genetic Algorithms (GA) are a broad class of minimization algorithms modeled after genetics and
evolution. Unlike local algorithms, such as the gradient descent algorithm, GA's are much
less likely to find and stay in a local minimum. This is a considerable advantage for a
large class of problems, including many applications in Particle Physics. I will explain how
GA's work and, as an example, use a GA to perform "optimal" multidimensional linear cuts.
Six Modified FoxWolfram Moments, H_{l}, are used to characterize the event topology at the collider
and a GA is employed to find the region in sixdimensional H_{l}space that enhances signal over
background for the sixjet decay of topquark pairs. I will also explain how Neural Networks
work and discuss how GA's can be used to improve the training of Neural Networks.
