Every real-world problem from economic to scientific and engineering fields is ultimately confronted with a common task, viz., optimization. Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. The goal of this book is to provide effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms. In this regard, five significant issues have been investigated: bridging the gap between theory and practice of GEAs, thereby providing practical design guidelines; demonstrating the practical use of the suggested road map; offering a useful tool to significantly enhance the exploratory power in time-constrained and memory-limited applications; providing a class of promising procedures that are capable of scalably solving hard problems in the continuous domain; and opening an important track for multiobjective GEA research that relies on decomposition principle. This book serves to play a decisive role in bringing forth a paradigm shift in future evolutionary computation.