Evolutionary Algorithms for VLSI CAD / Edition 1

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Overview

In VLSI CAD, difficult optimization problems have to be solved on a constant basis. Various optimization techniques have been proposed in the past. While some of these methods have been shown to work well in applications and have become somewhat established over the years, other techniques have been ignored.
Recently, there has been a growing interest in optimization algorithms based on principles observed in nature, termed Evolutionary Algorithms (EAs).
Evolutionary Algorithms in VLSI CAD presents the basic concepts of EAs, and considers the application of EAs in VLSI CAD. It is the first book to show how EAs could be used to improve IC design tools and processes. Several successful applications from different areas of circuit design, like logic synthesis, mapping and testing, are described in detail.
Evolutionary Algorithms in VLSI CAD consists of two parts. The first part discusses basic principles of EAs and provides some easy-to-understand examples. Furthermore, a theoretical model for multi-objective optimization is presented. In the second part a software implementation of EAs is supplied together with detailed descriptions of several EA applications. These applications cover a wide range of VLSI CAD, and different methods for using EAs are described.
Evolutionary Algorithms in VLSI CAD is intended for CAD developers and researchers as well as those working in evolutionary algorithms and techniques supporting modern design tools and processes.

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Editorial Reviews

Booknews
Synthesizes several years of research into circuit design and evolutionary algorithms at the Goethe University in Frankfurt, Germany. Discusses basic principles of evolutionary algorithms, characteristics of problem instances, and performance evaluation, illustrating them with simple examples. Then describes applications in a wide range of methods for designing large integrated circuits with computer-aided design tools, considering aspects of implementation and heuristic learning. Some of the material has been used in tutorials at Stanford and in Singapore during 1997. Annotation c. by Book News, Inc., Portland, Or.
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Product Details

  • ISBN-13: 9781441950406
  • Publisher: Springer US
  • Publication date: 12/3/2010
  • Edition description: Softcover reprint of hardcover 1st ed. 1998
  • Edition number: 1
  • Pages: 183
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.42 (d)

Table of Contents

Preface. Part I: Basic Principles. 1. Introduction. 2. Evolutionary Algorithms. 3. Characteristics of Problem Instances. 4. Performance Evaluation. Part II: Practice. 5. Implementation. 6. Applications of EAs. 7. Heuristic Learning. 8. Conclusions. References. Index.

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