This book introduces readers to genetic algorithms (GAs) with an emphasis on making the
concepts algorithms and applications discussed as easy to understand as possible. Further it
avoids a great deal of formalisms and thus opens the subject to a broader audience in
comparison to manuscripts overloaded by notations and equations.The book is divided into three
parts the first of which provides an introduction to GAs starting with basic concepts like
evolutionary operators and continuing with an overview of strategies for tuning and controlling
parameters. In turn the second part focuses on solution space variants like multimodal
constrained and multi-objective solution spaces. Lastly the third part briefly introduces
theoretical tools for GAs the intersections and hybridizations with machine learning and
highlights selected promising applications.
