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.
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.