Optimization problems occur regularly in chemistry. The problems are diverse and vary from selecting the best wavelength design for optimal spectroscopic concentration predictions to geometry optimization of atomic clusters and protein folding. Numerous optimization tactics have been explored to solve these problems. While most optimizers maintain the ability to locate global optima for simple problems, few are robust against local optima convergence with regard to hard or large scale optimization problems. Simulated annealing (SA) has shown a great tolerance to local optima convergence and is often called a global optimizer. The optimization algorithm has found wide use in numerous areas such as engineering, computer science, communication, image recognition, operation research, physics, and biology. Recently, SA and variations on it have shown considerable success in solving numerous chemical optimization problems. One thrust of this book is to demonstrate the utility of SA in a wide range chemical disciplines.