The Age of Analog Networks


  • Claudio Mattiussi Swiss Federal Institute of Technology in Lausanne (EPFL)
  • Daniel Marbach Swiss Federal Institute of Technology in Lausanne (EPFL)
  • Peter Dürr Swiss Federal Institute of Technology in Lausanne (EPFL)
  • Dario Floreano Swiss Federal Institute of Technology in Lausanne (EPFL)



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 time scales. 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 the 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 the reverse engineering of biological networks.




How to Cite

Mattiussi, C., Marbach, D., Dürr, P., & Floreano, D. (2008). The Age of Analog Networks. AI Magazine, 29(3), 63.