Subject
This volume, written by experts in the field, gives a
modern, rigorous and unified presentation of the
application of biological concepts to the design of novel
computing machines and algorithms. While science has as its
fundamental goal the understanding of Nature, the
engineering disciplines attempt to use this knowledge to
the ultimate benefit of Mankind. Over the past few decades
this gap has narrowed to some extent. A growing group of
scientists has begun engineering artificial worlds to test
and probe their theories, while engineers have turned to
Nature, seeking inspiration in its workings to construct
novel systems. The organization of living beings is a
powerful source of ideas for computer scientists and
engineers. This book studies the construction of machines
and algorithms based on natural processes: biological
evolution, which gives rise to genetic algorithms, cellular
development, which leads to self-replicating and
self-repairing machines, and the nervous system in living
beings, which serves as the underlying motivation for
artificial learning systems, such as neural networks.
Originality
This book is unique for the following reasons: It
follows a unified approach to bio-inspiration based on the
so-called POE model: phylogeny (evolution of species),
ontogeny (development of individual organisms), and
epigenesis (life-time learning). It is largely
self-contained, with an introduction to both biological
mechanisms (POE) and digital hardware (digital systems,
cellular automata). It is mainly applied to computer
hardware design.
Public
Undergraduate and graduate students, researchers,
engineers, computer scientists, and communication
specialists.
Contents
An Introduction to Bio-Inspired Machines - An
Introduction to Digital Systems - An Introduction to
Cellular Automata - Evolutionary Algorithms and their
Applications - Programming Cellular Machines by Cellular
Programming - Multiplexer-Based Cells - Demultiplexer-Based
Cells - Binary Decision Machine-Based Cells -
Self-Repairing Molecules and Cells - L-hardware: Modeling
and Implementing Cellular Development - Using L-systems -
Artificial Neural Networks: Algorithms and Hardware
Implementation - Evolution and Learning in Autonomous
Robotic Agents - Bibliography - Index.
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