Since the early days of civilization, scientists have been striving to identify the best from a set of possible alternatives. Such concept of optimization had been practiced by the Ancient Egyptians when building their pyramids and by Euclid of Alexandria when defining the shortest distance between a point and a line. The purpose of this book is to take the reader on a journey starting from Darwin's theory of evolution and ending in its modern application in robot control. Special emphasis is given to genetic algorithms (GAs) and their role in optimization. This comprises their history, structure, strengths, limitations as well as their application to the vibration and position control of flexible robot manipulators. Aside from being an informative text, the book aims at presenting the author's ideas for the enhancement of GAs. Two new techniques are presented: MAGA and EGA. The results show that the latter set a promising ground for further applications involving complex mathematical functions, multiple link robots and on-line control. This is due to its fast convergence, high precision and its ability to combine the merits of both global and local search methods.