Evolution of Analog Networks using Local String Alignment on Highly Reorganizable Genomes
We introduce and apply a genetic representation for analog electronic circuits based on the association of character strings extracted from the genome with the ter-minals and parameters of components, and the use of local string alignment to generate the connection between components. The representation produces a variable ge-nome length structure that tolerates the execution of ma-jor genome reorganization operators such as duplication and transposition, along with less disruptive ones such as character insertion, deletion and substitution. The repre-sentation can be applied also to other analog networks such as artificial neural networks, control systems, and biological genetic regulatory networks.
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Keywords: AGE ; Analog Genetic Encoding ; Neuroevolution ; Neuromodulation ; Evolution of Analog Electronic Circuits ; Reinforcement Learning ; Implicit Encoding ; Implicit Genetic Encoding ; Evolutionary Robotics
Zebulum, R.S. et al. (eds)
Record created on 2006-01-12, modified on 2016-08-08