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. Evolutionary Algorithms For Vlsi Cad Presents The Basic Ideas Of Eas And The Application Of Eas In Vlsi Cad Is Considered. It Is The First Book To Show How Eas Could Be Used To Improve Lc 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 For Vlsi Cad Is Intended For Cad Developers And Researchers As Well As People Who Are Working In Evolutionary Algorithms And Techniques Supporting Modern Design Tools And Processes. Front Matter....Pages i-x Front Matter....Pages 1-1 Introduction....Pages 3-10 Evolutionary Algorithms....Pages 11-17 Characteristics of Problem Instances....Pages 19-21 Performance Evaluation....Pages 23-41 Front Matter....Pages 43-43 Implementation....Pages 45-56 Applications of EAs....Pages 57-145 Heuristic Learning....Pages 147-163 Conclusions....Pages 165-166 Back Matter....Pages 167-183
