The Age of Analog Networks
A large class of systems of biological and technological relevance can be described as analog networks, that is, collections of dynamical devices interconnected by links of varying strength. Some examples of analog networks are genetic regulatory networks, metabolic networks, neural networks, analog electronic circuits, and control systems. Analog networks are typically complex systems which include nonlinear feedback loops and possess temporal dynamics at different timescales. When tackled by a human expert both the synthesis and reverse engineering of analog networks are recognized as knowledge-intensive activities, for which few systematic techniques exist. In this paper we will discuss the general relevance of the analog network concept and describe an evolutionary approach to the automatic synthesis and reverse engineering of analog networks. The proposed approach is called analog genetic encoding (AGE) and realizes an implicit genetic encoding of analog networks. AGE permits the evolution of human-competitive solutions to real-world analog network design and identification problems. This is illustrated by some examples of application to the design of electronic circuits, control systems, learning neural architectures, and to the reverse engineering of biological networks.
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Keywords: AGE ; Analog Genetic Encoding ; Implicit Encoding ; Implicit Genetic Encoding ; Analog Networks ; Evolutionary Computation ; Genetic Representation ; Analog Genetic Encoding ; Analog Circuit Synthesis ; Analog Network Synthesis ; Genetic Representation ; Neural Network Synthesis ; Genetic Regulatory Networks ; GRN ; Reverse Engineering ; Analog Genetic Encoding ; Genetic Representation ; Evolutionary Robotics
Record created on 2008-04-20, modified on 2016-08-08