||Genetic Algorithms (GA) have been a branch of Artificial Intelligence since the mid 1970's. Since then, many different kinds of GAs have been invented; however, most of these genetic algorithms are a crude representation of the evolutionary mechanisms from which they model. The Genetic Wavelet Algorithm is an Evolutionary Algorithm developed by Jeffery Freeman of Syncleus, Inc. that attempts to more accurately model the evolution than the traditional GAs. The purpose of this lecture is formally to define the Genetic Wavelet Algorithm and describe how what is required for it to be implemented. In addition, the performance of the Genetic Wavelet Algorithm will be compared to the classic Simple Genetic Algorithm on difficult instances of NP-Hard problems.