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Science arises from the very human desire to understand and control the world. Over the course of history, we humans have gradually built up a grand edifice of knowledge that enables us to predict, to varying extents, thenweather, the motions of the planets, solar and lunar eclipses, the courses of diseases, the rise and fall of economic growth, the stages of language development in children, and a vast panorama of other natural, social, and cultural phenomena. The advent of electronic computers has arguably been the most revolutionary development in the history of science and technology. This ongoing revolution is profoundly increasing our ability to predict and control nature in ways that were barely conceived of even half a century ago.It should be no surprise, then, that from the earliest days computers were applied not only to alculating missile trajectories and deciphering military codes but also to modeling the brain, mimicking human learning, and simulating biological evolution. These biologically motivated computing activities have waxed and waned over the years, but since the early 1980s they have all undergone a resurgence in the computation research community. The first has grown into the field of neural networks, the second into machine learning, and the third into what is now called "evolutionary computation," of which genetic algorithms are the most prominent example. An Introduction to Genetic Algorithms is divided into 6 chapters, which are Genetic Algorithms: An Overview; Genetic Algorithms in Problem Solving; Genetic Algorithms in Scientific Models; Theoretical Foundations of Genetic Algorithms; WHEN SHOULD A GENETIC ALGORITHM BE USED?; Conclusions and Future Directions