Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) 2005th Edition by Kenneth Price (PDF)

Description

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.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐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.

⭐I found the book quite informative. Differential Evolution is a population based optimization algorithm that is quite simple to implement and surprisingly effective. The book shows in detail the classical as well as several variants of the algorithm. In the book, the algorithm is well benchmarked using well known test functions. It is very useful when I want to compare with other algorithms. Needless to say, it provides information on appropriate parameter settings. It also describes some applications in detail. If you can borrow it from a library, you may not need to buy it. As for myself, as a researcher, it has been a handy reference.

⭐a good literature to learn about differential evolution. I am so glad for keep this book with me. Thanks a lot

⭐I am upgrading my rating from 3 stars to 4, six years after posting my original review. I wrote an application that has been in use for about 3 years now, using the JADE variant of DE (not described in the book). One problem the application had was not being able to handle constraints on combinations of parameters using constraint functions. In looking for a solution, I decided to re-read parts of the book. Lo and behold, there was a great description of Lampinen’s method for handling constraint functions. This method is very clever, effective, and surprisingly efficient. It worked out very well and solved a significant problem in my application. My conclusion now about the book is that beginners should probably look elsewhere for an introduction that’s easier to understand, but more experienced users, as I am now (but not when I originally wrote my review) will find some real gems here. My original review appears below.—I am of two minds about this book. First, it’s essential reading for anyone who wishes to use the Differential Evolution (DE) algorithm in any serious way, or create their own implementation of it. But the book is not nearly as good as it should be for learning DE.The nature of optimization is such that it will be of interest to a diverse group of people, such as:1) People who need to use an existing implementation as-is to solve a problem, and need some guidance about the meaning of various adjustable parameters, and how to adjust them to achieve the best possible results.2) People who need to solve a number of problems with DE, and may need to modify an existing implementation to do so.3) People writing applications that use DE to solve one or more problems, and need to make sure the DE implementation in their own code “just works”, so the application’s users need not concern themselves with tweaking the optimizer.4) People who wish to have a very deep knowledge of the subject, possibly creating new implementations of DE specific to their needs.To accommodate the diverse needs of various readers, a more “top down” approach is needed. While the book does this to some extent, there is a haphazard structure to the book that needs improvement.The book chapters are as follows, along with suggestions for improvement.Chapter 1 is an introduction to optimization, discusses the nature of the optimization problem itself, the distinction between local and global optimization, and provides a brief introduction to DE. This chapter is done very well, providing just the right balance between mathematical description and appeal to intuition. As is done throughout the book, excellent illustrations are provided. It’s a very good chapter not in need of modification.Chapter 2 describes the details of the algorithm. Part way into this chapter is where problems arise. There are three main reasons for the difficulty. First, DE has a number of variants, and these aren’t even spelled out clearly until the next chapter, in section 3.3.1. Second, concepts such as crossover are introduced by immediately spelling out details of the algorithm implementation without first explaining why we’d need or want to perform the crossover operation in the first place. Third, there are a number of categories of algorithm whose details are provided, yet some of these algorithms aren’t even used by DE, although the distinction is not made clear. These categories include the following:1) Algorithms common to all variants of DE2) Algorithms used only for one specific variant of DE3) Algorithms that aren’t part of DE at all, but may be useful in some other context4) Algorithms that aren’t part of DE and should be avoidedIn reading Chapter 2 I was often left wondering “Do I need to worry about this or not?” They need to move the general description of the algorithm variants from 3.3.1 forward into chapter 2, before the details of the algorithms are discussed. Any algorithm descriptions that are for something other than DE should only be done after the discussion of DE algorithms. On the positive side, Chapter 2 does include excellent illustrations that really aid understanding.Chapter 3, which deals with benchmarking, is misplaced. Chapter 4, which is given the odd name “Problem Domains”, belongs after the algorithm description in Chapter 2. “Problem Domains” deals with how to make use of DE to solve certain categories of problems, such as parameter quantization and constraints. These issues are very common, extremely important and must be figured out before one can do benchmarking. That said, Chapter 3 is fine as-is, but needs to be moved to a point in the book after that for which the solutions to most common problems are provided.Chapter 4 (“Problem Domains”) is well done too, but it might make sense to prioritize this chapter’s discussion by first dealing with the problem types for which DE excels, while leaving other type of problems until later.Chapter 5 on Architectural Aspects and Computing Environments is also well written and clear, dealing with such issues as parallel processors and systems with limited resources.Chapter 6 discusses the source code of the algorithm. I found having this chapter open when studying the C source code helped my understanding of the code a lot. The code itself, however, is not really suitable for production purposes, but is pretty good for learning the details of the algorithm. Although the authors speak of the alleged benefits of the so-called “Hungarian notation”, this notation is becoming passe, even to the extent that one of its former advocates, Charles Petzold of Microsoft, now discourages its use. Do a web search of “Hungarian notation sucks” to find his blog entry on the subject. The types of errors this notation is alleged to prevent can be prevented much better in practice by compiling with the highest warning level and ensuring that no warnings show up.Chapter 7 discusses specific problems that have used DE for their solution. Understanding some of them requires domain knowledge that I don’t have, but I was delighted to find that one of the authors, Rainer Storn, uses DE in his FIWIZ software to design digital filters having non-traditional design requirements. This is exactly the problem I am using DE for, so I was quite glad to see that section of the chapter. This is a good chapter.To sum up, the individual chapters, with the exception of Chapter 2, are generally well-done, but the organization needs improvement. This haphazard organization may be due to the book having multiple authors, resulting in a lack of a clear vision for the book as a whole. After finishing the book, I was still confused about a number of issues, and these weren’t cleared up until I read the book on DE by Feoktistov.

⭐Some one who wants to begin with DE. This the good starting point. Book started with good conceptual backgroud and carried away with codeing details of DE. Kenneth puts enough efforts to clear concept behind DE. Only thing missing is that book demands little background with GAs, EAs and optimization theory.Other wise nice book for those who are familiarized with concept of evolutionary techniques.

⭐Not found.

Keywords

Free Download Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) 2005th Edition in PDF format
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) 2005th Edition PDF Free Download
Download Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) 2005th Edition 2005 PDF Free
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) 2005th Edition 2005 PDF Free Download
Download Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) 2005th Edition PDF
Free Download Ebook Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) 2005th Edition