An authoritative account of adaptive evolvable systems operating in a changing environment. The proposed component approach for the design of evolvable systems, the mathematical theory of evolvable machines, and the idea of virtual reconfigurable circuits have opened a way towards a better understanding of evolvable systems and to the design of more adaptive, competitive and innovative engineering products. In particular, topics such as the computational power of evolvable systems, the design of complex human-competitive digital circuits and the implementation of a one-clock partial reconfiguration in a common field-programmable gate array are introduced, investigated and implemented. The application domain is the adaptive image preprocessing.

Provides new insights to the theory, software implementations and hardware realizations of evolvable systems Includes supplementary material: sn.pub/extras

Autorentext

Lukas Sekanina received MSc degree in Computer Science and Engineering and PhD degree in Information Technology from Brno University of Technology, Czech Republic, in 1999 and 2002 , respectively. He was a visiting lecturer with Pennsylvania State University, USA, and a visiting researcher with Department of Informatics, University of Oslo, Norway, in 2001. He is author or co-author of more than 20 refereed conference papers mainly on evolvable hardware and bio-inspired computing. He was awarded Siemens Awards 1999 and 2000 and The best paper award at IEEE Design and Diagnostics of Electronic Circuits and System workshop 2002. Currently he is an assistant professor with Faculty of Information Technology, Brno University of Technology. His research interests focus on theory, design and implementations of bio-inspired computational systems.

Inhalt
1 Introduction.- 1.1 Natural Computing.- 1.2 Bioinspired Hardware.- 1.3 Motivation for Research.- 2 Reconfigurable Hardware.- 2.1 Digital Cicuits.- 2.2 Digital Circuit Design.- 2.3 Field Programmable Gate arrays.- 2.4 Hardware Reused as Software.- 2.5 Reconfigurable Computing.- 2.6 Nanotechnology.- 2.7 Cell Matrix.- 2.8 Summary.- 3 Evolutionary Algorithms.- 3.1 Introduction.- 3.2 Variant of Evolutionary Algorithms.- 3.3 Some Other Features of Evolutionary Algorithms.- 3.4 Evolutionary Design and Optimization.- 3.5 The Evolutionary Algorithm Design.- 3.6 Formal Approach.- 3.7 Summary.- 4 Evolvable Hardware.- 4.1 Basic Concept.- 4.2 Cartesian Genetic Programming.- 4.3 Features of Cartesian Genetic Programming.- 4.4 From Chromosome to Fitness Value.- 4.5 Fitness Function.- 4.6 Applications and Degree of Hardware Implementation.- 4.7 Promising Results.- 4.8 Major Current Problems and Potential Solutions.- 4.9 Summary.- 5 Towards Evolvable Components.- 5.1 Component Approach to Problem Solving.- 5.2 Evolvable Components.- 5.3 Hardware Implementation.- 5.4 Extension of Evolvable Components.- 5.5 Summary.- 6 Evolvable Computational Machines.- 6.1 Computational Machines and Evolutionary Design.- 6.2 Cellular Automata.- 6.3 General Evolvable Computational Machine.- 6.4 Dynamic Environment.- 6.5 Evolvable Computational System.- 6.6 Properties of Evolvable Machines.- 6.7 The Computational Power.- 6.8 Summary.- 7 An Evolvable Component for Image Pre-processing.- 7.1 Motivation and Problem Specification.- 7.2 The Image Filter Design.- 7.3 Analysis of Reconfigurability and Size of the Search Space.- 7.4 Evolutionary Design: Experimental Framework.- 7.5 Filters for Smoothing.- 7.6 Other Image Operators.- 7.7 Dynamics Environment.- 7.8 A Note on a Single Filter Design.- 7.9 Summary.- Virtual Reconfigurable Devices.- 8.1 Chip on Top of a Chip.- 8.2 Architecture of Virtual Reconfigurable Circuits.- 8.3 Implementation Costs.- 8.4 Speeding up the Evolutionary Design.- 8.5 Genetic Unit.- 8.6 Physical Realization.- 8.7 Discussion.- 8.8 Summary.- 9 Concluding Statements.- 9.1 The Approach.- 9.2 The Obtained Results.- 9.3 Future Work.- References.