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Multi-Objective Optimization Using Evolutionary Algorithms Paperback – March 2, 2009

8 customer reviews
ISBN-13: 978-0470743614 ISBN-10: 0470743611 Edition: 1st

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Editorial Reviews

Review

"Deb's book is complete, eminently readable, and the coverage is scholarly and thorough. It is my pleasure and duty to urge you to buy this book, read it, use it and enjoy it." (David E. Goldberg, University of Illinois at Urbana-Champaign, USA)

"...discusses two multi-objective optimization procedures, namely the ideal procedure and the preference-based one." (Zentralblatt MATH, Vol. 970, 2001/20)

Excerpt from Preface: "...provides an extensive discussion on the principles of multi-objective optimization and on a number of classical approaches." (Mathematical Reviews, 2002)

"...As a survey, this book is exemplary and forms an essential resource for EMO researchers at the present time." (Siam Review, Vol.44, No.3, 2002)

"...a readable account of a topic of current interest in operational research." (Mathematika, No.48, 2001)

??an outstandingly well-organized and clearly written account of the subject? (The Mathematical Gazette, July 2003)

--This text refers to the Hardcover edition.

From the Back Cover

Multi-Objective Optimization using Evolutionary Algorithms

Kalyanmoy Deb
Indian Institute of Technology, Kanpur, India

The Wiley Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians and scientists.

Multi-Objective Optimization using Evolutionary Algorithms

Kalyanmoy Deb
Indian Institute of Technology, Kanpur, India

Evolutionary algorithms are very powerful techniques used to find solutions to real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run.

  1. Comrephensive coverage of this growing area of research.
  2. Carefully introduces each algorithm with examples and in-depth discussion.
  3. Includes many applications to real-world problems, including engineering design and scheduling.
  4. Includes discussion of advanced topics and future research.
  5. Accessible to those with limited knowledge of multi-objective optimization and evolutionary algorithms

Provides an extensive discussion on the principles of multi-objective optimization and on a number of classical approaches.

This integrated presentation of theory, algorithms and examples will benefit those working in the areas of optimization, optimal design and evolutionary computing.

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Product Details

  • Paperback: 544 pages
  • Publisher: Wiley; 1 edition (March 2, 2009)
  • Language: English
  • ISBN-10: 0470743611
  • ISBN-13: 978-0470743614
  • Product Dimensions: 6.8 x 1.2 x 9.7 inches
  • Shipping Weight: 1.9 pounds (View shipping rates and policies)
  • Average Customer Review: 3.9 out of 5 stars  See all reviews (8 customer reviews)
  • Amazon Best Sellers Rank: #436,156 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

16 of 16 people found the following review helpful By A Customer on September 28, 2001
Format: Hardcover
Kalyanmoy Deb has put together a great summary of the state of affairs in multiobjective genetic algorithms. Should you be an engineer or a scientist involved in the optimization of any design of sizeable complexity, you should read this book and become familiar with the techniques that have evolved over the last decade into powerful methods of optimization. This book is in many many ways bridging the gap from Michalewicz's and Fogel's book ("How to solve it") to the more modern era of this field (eg late nineties up to now...). So whereas those two authors never really considered multiobjective genetic algorithms, Deb plows through with the great expertize of a (perhaps even "the") leading researcher in that domain. This is a great book of _receipes_ with the level of details necessary to make use of them. It's a "how to" book; this is the one you have cracked open on your desk while you're hard coding it all up. However, it's not very well written with the prose being very terse and basically quite unengaging. But so what! In some sense yes perhaps, but Michalewicz and Fogel made a point that one can write technical litterature that one can also read. Perhaps they went overboard... in any case, Deb's book is about algorithms so who cares about whether the book puts you to sleep and it can do that, unfortunately. Apart from the unengaging style and the paucity of depth in the examples scope, the real problem with the book is not with the book itself, it's with the field of multiobjective optimization based on evolutionary methods. It's fairly evident that there is not much of any sort of fundamental understanding available at this time in support of why evolutionary techniques do work well, and they do, sometimes...Read more ›
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11 of 11 people found the following review helpful By Marco Farina on July 23, 2001
Format: Hardcover
This is the first complete and updated text on Multi-objective Evolutionary Algorithms (MOEAs), covering all major areas clearly, thoughtfully and thoroughly. Thanks to the development of evolutionary computation MOEAs are now a well established technique for multi-objective optimization that finds multiple effective solutions in a single run. The widely interdisciplinary interest of engineers, scientists and mathematicians towards MOEAs has been evident during the first international conference on this topic (EMO2001,Zurich). The book is extremely useful for researchers working on multi-objective optimization in all branches of engineering and sciences, that will find a complete description of all available methodologies, starting from a detailed description and criticism of classical methods, towards a deep treating of the most advanced evolutionary techniques. Moreover several analytical test cases are given, covering all difficulties a MOEA encounters when converging towards the Pareto Optimal front. This set of test problems, together with several performance measurement parameters are essential when testing a new strategy before its application to a real-world problem. Despite the detail in advanced topics, Deb's book may be also used as a reference-book for a post-graduate course thanks to the scholarly coverage of basic arguments. As a final remark I strongly suggest everyone working on evolutionary computation and optimization to keep this book on the desk.
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2 of 2 people found the following review helpful By Mick Twohig on March 26, 2013
Format: Paperback Verified Purchase
I purchased the paperback version of this book in March 2013, and the print quality is terrible. I assumed it was just a bad copy, and had Amazon send a replacement copy. However, the replacement is just as bad. Some issues are uneven font facing, which makes some of the descenders on letters very narrow, and also makes it tricky to follow sub- and super-scripted variables. The diagramming looks to be poor quality in this version also. As an example of very bad quality, consider page 18 of the book (both copies I received had the same issue) - it looks like a low resolution scan or photocopy.
It is a great pity - the content of the book itself looks terrific, however, it is badly let down by the quality of this printing. I'd recommend looking for other printings of this book.
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By victor on February 1, 2014
Format: Hardcover Verified Purchase
The best book of multiobjective optimization for an engineer who does not have a deep math background. It suits very well for some one with good programming skills in fortran, c or matlab .
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