Global Optimization Algorithms - Theory and Application

Global Optimization Algorithms - Theory and Application

This book elaborates on many of the basic principles in global optimization, evolutionary algorithms, and genetic programming and describes how they can efficiently be realized in software.

Publication date: 11 Jul 2007

ISBN-10: n/a

ISBN-13: n/a

Paperback: n/a

Views: 18,222

Type: N/A

Publisher: n/a

License: GNU Free Documentation License Version 1.2

Post time: 12 Jul 2007 06:05:08

Global Optimization Algorithms - Theory and Application

Global Optimization Algorithms - Theory and Application This book elaborates on many of the basic principles in global optimization, evolutionary algorithms, and genetic programming and describes how they can efficiently be realized in software.
Tag(s): Artificial Intelligence
Publication date: 11 Jul 2007
ISBN-10: n/a
ISBN-13: n/a
Paperback: n/a
Views: 18,222
Document Type: N/A
Publisher: n/a
License: GNU Free Documentation License Version 1.2
Post time: 12 Jul 2007 06:05:08
Summary/Excerpts of (and not a substitute for) the GNU Free Documentation License Version 1.2:
Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.  A copy of the license is included in the section entitled "GNU Free Documentation License".

Click here to read the full license.
Excerpts from the Preface:

The e-book is devoted to global optimization algorithms, which are methods to find optimal solutions for given problems. It especially focuses on evolutionary computation, discussing evolutionary algorithms, genetic algorithms, genetic programming, learning classifier systems, evolution strategy, differential evolution, particle swarm optimization, and ant colony optimization. It also elaborates on techniques like simulated annealing, hill climbing, tabu search, and random optimization.

The book can help students by also providing the related background in, for example, stochastic and theoretical computer science. Furthermore, application examples as well as a Java implementation of the introduced methods are discussed. The book may however also be interesting for researchers since it also contains in-depth information in many areas and a set of huge literature references.

This book is updated and improved regularly.




About The Author(s)


Thomas Weise is a computer scientist at the USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI) belonging to the School of Computer Science and Technology (SCST) of the University of Science and Technology of China (USTC). He have worked on research in the field optimization algorithms, mainly centered around Evolutionary Computation.

Thomas Weise

Thomas Weise is a computer scientist at the USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI) belonging to the School of Computer Science and Technology (SCST) of the University of Science and Technology of China (USTC). He have worked on research in the field optimization algorithms, mainly centered around Evolutionary Computation.


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