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Evolvable Systems: From Biology to Hardware

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Cover of 'Evolvable Systems: From Biology to Hardware'

Table of Contents

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    Book Overview
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    Chapter 34 Iconic learning in networks of logical neurons
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    Chapter 35 Hardware requirements for fast evaluation of functions learned by Adaptive Logic Networks
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    Chapter 36 FPGA as a key component for reconfigurable system
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    Chapter 37 Phylogeny, ontogeny, and epigenesis: Three sources of biological inspiration for softening hardware
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    Chapter 38 Promises and challenges of Evolvable hardware
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    Chapter 39 Designing evolware by cellular programming
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    Chapter 40 Online autonomous evolware
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    Chapter 41 Speeding-up digital ecologies evolution using a hardware emulator: Preliminary results
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    Chapter 42 Challenges of evolvable systems: Analysis and future directions
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    Chapter 43 Functional organisms growing on silicon
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    Chapter 44 Logical universality and self-reproduction in reversible cellular automata
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    Chapter 45 Data compression based on Evolvable hardware
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    Chapter 46 ATM cell scheduling by function level evolvable hardware
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    Chapter 47 An evolutionary robot navigation system using a gate-level evolvable hardware
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    Chapter 48 Genetic evolution of a logic circuit which controls an autonomous mobile robot
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    Chapter 49 Autonomous robot with evolving algorithm based on biological systems
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    Chapter 50 Memory-based neural network and its application to a mobile robot with evolutionary and experience learning
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    Chapter 51 Multiple genetic algorithm processor for hardware optimization
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    Chapter 52 NGEN: A massively parallel reconfigurable computer for biological simulation: Towards a self-organizing computer
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    Chapter 53 Architecture of cell array neuro-processor
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    Chapter 54 Special-purpose brainware architecture for data processing
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    Chapter 55 Evolvable Hardware: An outlook
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    Chapter 56 Reuse, parameterized reuse, and hierarchical reuse of substructures in evolving electrical circuits using genetic programming
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    Chapter 57 Machine learning approach to gate-level Evolvable Hardware
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    Chapter 58 Evolvable systems in hardware design: Taxonomy, survey and applications
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    Chapter 59 From some tasks to biology and then to hardware
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    Chapter 60 Adaptive equalization of digital communication channels using Evolvable Hardware
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    Chapter 61 An evolved circuit, intrinsic in silicon, entwined with physics
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    Chapter 62 Through the labyrinth evolution finds a way: A silicon ridge
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    Chapter 63 Hardware evolution system introducing dominant and recessive heredity
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    Chapter 64 CAM-Brain: A new model for atr's cellular automata based artificial brain project
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    Chapter 65 Evolution of a 60 decibel op amp using genetic programming
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    Chapter 66 Evolution of binary decision diagrams for digital circuit design using genetic programming
Attention for Chapter 61: An evolved circuit, intrinsic in silicon, entwined with physics
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Chapter title
An evolved circuit, intrinsic in silicon, entwined with physics
Chapter number 61
Book title
Evolvable Systems: From Biology to Hardware
Published in
Evolvable Systems: From Biology to Hardware, January 1997
DOI 10.1007/3-540-63173-9_61
Book ISBNs
978-3-54-063173-6, 978-3-54-069204-1
Authors

Adrian Thompson, Thompson, Adrian

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X Demographics

The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 132 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 6 5%
Germany 3 2%
Spain 2 2%
Switzerland 1 <1%
Italy 1 <1%
Brazil 1 <1%
Uruguay 1 <1%
France 1 <1%
Estonia 1 <1%
Other 3 2%
Unknown 112 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 45 34%
Researcher 18 14%
Student > Master 17 13%
Student > Bachelor 12 9%
Student > Doctoral Student 8 6%
Other 23 17%
Unknown 9 7%
Readers by discipline Count As %
Computer Science 35 27%
Engineering 29 22%
Physics and Astronomy 15 11%
Agricultural and Biological Sciences 9 7%
Neuroscience 7 5%
Other 28 21%
Unknown 9 7%