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OverviewIn 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. Full Product DetailsAuthor: Rolf DrechslerPublisher: Springer-Verlag New York Inc. Imprint: Springer-Verlag New York Inc. Edition: Softcover reprint of hardcover 1st ed. 1998 Dimensions: Width: 15.50cm , Height: 1.00cm , Length: 23.50cm Weight: 0.454kg ISBN: 9781441950406ISBN 10: 1441950400 Pages: 184 Publication Date: 03 December 2010 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand We will order this item for you from a manufactured on demand supplier. Table of ContentsI Basic Principles.- 1 Introduction.- 2 Evolutionary Algorithms.- 3 Characteristics of Problem Instances.- 4 Performance Evaluation.- II Practice.- 5 Implementation.- 6 Applications of Eas.- 7 Heuristic Learning.- 8 Conclusions.- References.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |