Bio-inspired Computing Machines: Towards Novel Computational Architectures
Presses polytechniques et universitaires romandes, Jan 1, 1998 - Algorithmes génétiques - 372 pages
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.
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An Introduction to BioInspired Machines
An Introduction to Digital Systems
An Introduction to Cellular Automata
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adaptive architecture array artificial neural networks artificial organism automaton behavior binary decision diagrams binary decision machine binary decision tree BIODULE biological bits blocks cellular automata cellular differentiation cellular programming Chapter circuit clock signal complete computation configuration connections coordinates corresponding crossover defined described dynamic element encoding environment evolution evolutionary algorithms evolved example execution fault Figure fitness flip-flop flowchart FPGA FPPA function gene genetic algorithms genome genotype global grid hardware implementation individuals initial input instruction integrated circuits interconnection Karnaugh map L-system layer learning algorithm logic loop memory microprogram MICTREE cell module molecule mother cell multicellular multiplexer mutation MUXTREE neighbors neural network neuron non-uniform one-dimensional operation output parallel parameters perceptron performance population possible problem processor random number realization representation robot rule selection self-repair self-replication sequential solution space string sub-program synaptic synchronous task truth table Turing machine two-dimensional up-down counter variable