000121372 001__ 121372
000121372 005__ 20190316234234.0
000121372 02470 $$2ISI$$a000260368400006
000121372 02470 $$2DAR$$a13800
000121372 037__ $$aARTICLE
000121372 245__ $$aThe Age of Analog Networks
000121372 269__ $$a2008
000121372 260__ $$c2008
000121372 336__ $$aJournal Articles
000121372 520__ $$aA 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.
000121372 6531_ $$aAGE
000121372 6531_ $$aAnalog Genetic Encoding
000121372 6531_ $$aImplicit Encoding
000121372 6531_ $$aImplicit Genetic Encoding
000121372 6531_ $$aAnalog Networks
000121372 6531_ $$aEvolutionary Computation
000121372 6531_ $$aGenetic Representation
000121372 6531_ $$aAnalog Genetic Encoding
000121372 6531_ $$aAnalog Circuit Synthesis
000121372 6531_ $$aAnalog Network Synthesis
000121372 6531_ $$aGenetic Representation
000121372 6531_ $$aNeural Network Synthesis
000121372 6531_ $$aGenetic Regulatory Networks
000121372 6531_ $$aGRN
000121372 6531_ $$aReverse Engineering
000121372 6531_ $$aAnalog Genetic Encoding
000121372 6531_ $$aGenetic Representation
000121372 6531_ $$aEvolutionary Robotics
000121372 700__ $$0241582$$aMattiussi, Claudio$$g140974
000121372 700__ $$aMarbach, Daniel
000121372 700__ $$0243223$$aDürr, Peter$$g167254
000121372 700__ $$0240742$$aFloreano, Dario$$g111729
000121372 773__ $$j29$$k3$$q63--76$$tAI Magazine
000121372 8564_ $$s836036$$uhttps://infoscience.epfl.ch/record/121372/files/MattiussiMarbachDurrFloreano2008.pdf$$zn/a
000121372 909C0 $$0252161$$pLIS$$xU10370
000121372 909CO $$ooai:infoscience.tind.io:121372$$pSTI$$particle$$qGLOBAL_SET
000121372 917Z8 $$x255330
000121372 937__ $$aLIS-ARTICLE-2008-012
000121372 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000121372 980__ $$aARTICLE