About this book
The human being aspires to the best possible performance. Both individuals and enterprises are looking for optimal—in other words, the best possible—solutions for situations or problems they face. Most of these problems can be expressed in mathematical terms, and so the methods of optimization undoubtedly render a significant aid.
In cases where there are many local optima; intricate constraints; mixed-type variables; or noisy, time-dependent or otherwise ill-defined functions, the usual methods don’t give satisfactory results. Are you seeking fresh ideas or more efficient methods, or do you perhaps want to be well-informed about the latest achievements in optimization? If so, this book is for you.
This book develops a unified insight on population-based optimization through Differential Evolution, one of the most recent and efficient optimization algorithms. You will find, in this book, everything concerning Differential Evolution and its application in its newest formulation. This book will be a valuable source of information for a very large readership, including researchers, students and practitioners. The text may be used in a variety of optimization courses as well.
- Neoteric view of Differential Evolution
- Unique formula of global optimization
- The best known metaheuristics through the prism of Differential Evolution
- Revolutionary ideas in population-based optimization
Differential Evolution will be of interest to students, teachers, engineers, and researchers from various fields, including computer science, applied mathematics, optimization and operations research, artificial evolution and evolutionary algorithms, telecommunications, engineering design, bioinformatics and computational chemistry, chemical engineering, mechanical engineering, electrical engineering, and physics.
- DOI https://doi.org/10.1007/978-0-387-36896-2
- Copyright Information Springer Science+Business Media, LLC 2006
- Publisher Name Springer, Boston, MA
- eBook Packages Mathematics and Statistics Mathematics and Statistics (R0)
- Print ISBN 978-0-387-36895-5
- Online ISBN 978-0-387-36896-2
- Series Print ISSN 1931-6828
- Buy this book on publisher's site