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New Ideas in Optimisation (Advanced Topics in Computer Science) Paperback – 1 Oct 1999


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Product details

  • Paperback: 450 pages
  • Publisher: McGraw-Hill Inc.,US (1 Oct. 1999)
  • Language: English
  • ISBN-10: 0077095065
  • ISBN-13: 978-0077095062
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Bestsellers Rank: 2,309,265 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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Product Description

From the Publisher

Each of the new techniques is introduced by either its inventor or a pioneer in its applications
An active website provides source code and demonstrations for many of the new techniques discussed in the book
Tutorial approach with examples and problems
Detailed algorithms in pseudocode

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Most Helpful Customer Reviews

6 of 6 people found the following review helpful By A Customer on 3 April 2000
Format: Paperback
I have chosen this book as the main textbook for my masters course "Modern Heuristic Techniques", Computer Science, Australian Defence Force Academy, UC, University of New South Wales. The book puts together, in an excellent way, recent advances in the theory of heuristic search. Although the title emphasises optimisation, the prospective reader should be aware of the fact that problems in many domains (such as machine learning, conventional operational research, speech recognition, ... Etc.) are optimisation problems in the first place. While I was struggling to collect easy-to-understand papers for my students, this book came to save my time, my efforts, as well as my students. It was important for me to find a textbook, which covers non-conventional topics such as GA, EA, SA, ... Etc. Being written by the people who invented the novel techniques, this book matched all my expectations. Apart from minor personal comments on the presentation of some algorithms, the book is very well presented. I do encourage people teaching for post-graduates in the fields of operational research, machine learning, search, ... Etc, to use this book for their course. Finally, I would like to thank Mc-Graw Hill for their prompt delivery of the book and David Corne for his help.
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5 of 5 people found the following review helpful By Dr. Rainer Storn on 13 Jan. 2000
Format: Paperback
As I am one of the contributors of this book I should not be the one who provides a rating because I am biased. I feel, though that the information provided about the book is insufficient and therefore I would like to provide the information as given by the publisher McGraw-Hill (if this is permitted).
Synopsis: Optimisation is a pivotal aspect of software design. The techniques treated in this book represent the leading edge of research as elucidated by the leading researchers, many of whom are the originators of the methods. The volume editors are experienced and respected researchers and the subject is one of growing interest in advanced undergraduate and postgraduate programmes.There are a collection of well-known modern optimisation methods being researched and applied to real problems worldwide. These include a variety of local search methods (hillclimbing, simulated annealing, tabu search, ...) and so-called evolutionary computation methods (genetic algorithms, genetic programming, evolutionary programming...). In recent years, a range of novel ideas have emerged in this research community, proposing new algorithms which are interestingly different from the current well-studied crop. In particular, these new ideas include: Ant Colony based optimisation, Scatter Search, Differential Evolution, Immune System Methods, Optima Linking, and Parallel Distributed Genetic Programming.
Key Features: Tutorial approach with examples and problems| Detailed algorithms in pseudocode
Contents: Section 1: Ant Colony Optimisation; Section 2: Differential Evolution; Section 3: Scatter Search and Path Relinking; Section 4: Immune System Methods; Section 5: Memetic Algorithms; Section 6: Emerging Techniques and Extensions: Parallel Distributed Genetic Programming; Co-evolutionary methods; Guided Local Search; Stepwise Adaptive Weight Algorithms; Particle Swarm Methods
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