- 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:
2,849,728 in Books (See Top 100 in Books)
- #5103 in Books > Computers & Internet > Computer Science > Programming > Software Design, Testing & Engineering > Software Architecture
- #5154 in Books > Computers & Internet > Computer Science > Programming > Software Design, Testing & Engineering > Functional Programming
- #7823 in Books > Computers & Internet > Software & Graphics > Software Design & Development
- See Complete Table of Contents
New Ideas in Optimisation (Advanced Topics in Computer Science) Paperback – 1 Oct 1999
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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
Top Customer Reviews
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