Genetic Algorithm (GA) is an arti cial intelligence procedure. It is based on the
theory of natural selection and evolution. It is a heuristic search technique used
in computing and Arti cial Intelligence to nd 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 nding a solution in a very complex search
space, even implemented in supercomputers. Genetic Algorithm is a robust
search method requiring little information to search e ectively 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 elds of
specialization, who need some optimization techniques in their work and who
may be using Genetic Algorithms in their applications for the rst time. These
applications may be of useful to many other people who are getting familiar with
the subject of Genetic Algorithms.