I bought the book simply because the authors are the original developers of the algorithm, and hope to get some more information than what I learned from the literature (isolated individual publications over the years).I am a little disappointed, and feeling regretted spending nearly $70 on this book. That said, though, I would like to give a 3.75 stars if amazon allows me to do so. In general, the English seems not very well polished sometimes could hinder the understanding. The code can be used to help understand the algorithm but be warned, they might not be amenable for direct use in any serious software development. I spend more than 3 days reading the book but I feel what I got from the book is not even comparable to what I learned in just 1 hours from for example the following paperJouni Lampinen and Ivan Zelinka, "Mixed Variable Non-linear Optimization By Differential Evolution". The paper gives very clear and concise description of DE. (The author of the paper was the last author of the book). Finally, it is my opinion that: if you are completely new to Differential Evolution, it is not advisable to spend any money on this book. You would easily get lost by the overwhelming implementation details. For the purpose of learning Differential Evolution, relevant papers, wikipedia, or visiting various links on the subject would help you even better learn the algorithm. As a matter of fact (at least to me) the original papers by these authors are far more better (in terms of conciseness and clarity) and accessible than what they cook in this book. However, if you already have some exposure to Differential Evolution,but would like to know the specific implementation details or more in-depth theoretical discussions, the book can provide something more, especially regarding the implementation details such as initialization, crossover, scaling factor etc that not readily available (or omitted) from isolated publications. Problems demanding globally optimal solutions are ubiquitous, yet many are intractable when they involve constrained functions having many local optima and interacting, mixed-type variables.The differential evolution (DE) algorithm is a practical approach to global numerical optimization which is easy to understand, simple to implement, reliable, and fast. Packed with illustrations, computer code, new insights, and practical advice, this volume explores DE in both principle and practice. It is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimization.A companion CD includes DE-based optimization software in several programming languages, including C and MATLAB. "Problems demanding globally optimal solutions are ubiquitous, yet many are intractable when they involve constrained functions having many local optima and interacting mixed-type variables." "The differential evolution (DE) algorithm is a practical approach to global numerical optimization which is easy to understand, simple to implement, reliable, and fast. Packed with illustrations, computer code, new insights, and practical advice, this volume explores DE in both principle and practice. It is a valuable resource for professionals need a proven optimizer and for students wanting an evolutionary perspective on global numerical optimization."--BOOK JACKET Problems demanding globally optimal solutions are ubiquitous, yet many are intractable when they involve constrained functions having many local optima and interacting, mixed-type variables. The differential evolution (DE) algorithm is a practical approach to global numerical optimization which is easy to understand, simple to implement, reliable, and fast. Packed with illustrations, computer code, new insights, and practical advice, this volume explores DE in both principle and practice. It is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimization.
