Introduction to Genetic Algorithms

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.

Part One: Basic Concepts

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.

Part Two: Solution Space Variants

In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces.

Part Three: Theoretical Tools and Applications

Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.