Bio-Inspired Comp.Machine

SubjectThis volume, written by experts in the field, gives amodern, rigorous and unified presentation of theapplication of biological concepts to the design of novelcomputing machines and algorithms. While science has as itsfundamental goal the understanding of Nature, theengineering disciplines attempt to use this knowledge tothe ultimate benefit of Mankind. Over the past few decadesthis gap has narrowed to some extent. A growing group ofscientists has begun engineering artificial worlds to testand probe their theories, while engineers have turned toNature, seeking inspiration in its workings to constructnovel systems. The organization of living beings is apowerful source of ideas for computer scientists andengineers. This book studies the construction of machinesand algorithms based on natural processes: biologicalevolution, which gives rise to genetic algorithms, cellulardevelopment, which leads to self-replicating andself-repairing machines, and the nervous system in livingbeings, which serves as the underlying motivation forartificial learning systems, such as neural networks.OriginalityThis book is unique for the following reasons: Itfollows a unified approach to bio-inspiration based on theso-called POE model: phylogeny (evolution of species),ontogeny (development of individual organisms), andepigenesis (life-time learning). It is largelyself-contained, with an introduction to both biologicalmechanisms (POE) and digital hardware (digital systems,cellular automata). It is mainly applied to computerhardware design.PublicUndergraduate and graduate students, researchers,engineers, computer scientists, and communicationspecialists.ContentsAn Introduction to Bio-Inspired Machines - AnIntroduction to Digital Systems - An Introduction toCellular Automata - Evolutionary Algorithms and theirApplications - Programming Cellular Machines by CellularProgramming - Multiplexer-Based Cells - Demultiplexer-BasedCells - Binary Decision Machine-Based Cells -Self-Repairing Molecules and Cells - L-hardware: Modelingand Implementing Cellular Development - Using L-systems -Artificial Neural Networks: Algorithms and HardwareImplementation - Evolution and Learning in AutonomousRobotic Agents - Bibliography - Index.