Genetic Algorithm (GA) is an artificial intelligence procedure. It is based on the theory of natural selection and evolution. It is a heuristic search technique used in computing and Artificial Intelligence to find optimized solutions to search problems using techniques inspired by evolutionary biology: mutation, selection, reproduction and recombination. Genetic Algorithms have been applied in science, engineering, business and social sciences. Traditional methods of search and optimization are too slow in finding a solution in a very complex search space, even implemented in supercomputers. Genetic Algorithm is a robust search method requiring little information to search effectively in a large or poorly-understood search space. In particular a genetic search progress through a population of points in contrast to the single point of focus of most search algorithms. Moreover, it is useful in the very tricky area of nonlinear problems. Its intrinsic parallelism (in evaluation functions, selections and so on) allows the uses of distributed processing machines. This book, Genetic Algorithms in Applications, may be of immense useful to engineers and scientists in various fields of specialization, who need some optimization techniques in their work and who may be using Genetic Algorithms in their applications for the first time. These applications may be of useful to many other people who are getting familiar with the subject of Genetic Algorithms.