Evolutionary Algorithms for VLSI CAD by Rolf DrechslerEvolutionary Algorithms for VLSI CAD by Rolf Drechsler

Evolutionary Algorithms for VLSI CAD

byRolf Drechsler

Paperback | December 3, 2010

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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.
Title:Evolutionary Algorithms for VLSI CADFormat:PaperbackDimensions:184 pagesPublished:December 3, 2010Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:1441950400

ISBN - 13:9781441950406

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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.