@inbook { koza:2000:idas, title = {Automatic design of analog electrical circuits using genetic programming}, booktitle = {Intelligent Data Analysis in Science}, editor = {Hugh Cartwright}, year = {2000}, pages = {172--200}, publisher = {Oxford University Press}, type = {incollection}, chapter = {8}, address = {Oxford}, abstract = {The design (synthesis) of analog electrical circuits entails the creation of both the topology and sizing (numerical values) of all of the circuit's components. There has previously been no general automated technique for automatically designing an analog electrical circuit from a high-level statement of the circuit's desired behavior. This chapter introduces genetic programming and shows how it can be used to automate the design of both the topology and sizing of a suite of five prototypical analog circuits, including a lowpass filter, a tri-state frequency discriminator circuit, a 60 dB amplifier, a computational circuit for the square root, and a time-optimal robot controller circuit. The problem-specific information required for each of the eight problems is minimal and consists primarily of the number of inputs and outputs of the desired circuit, the types of available components, and a fitness measure that restates the high-level statement of the circuit's desired behavior as a measurable mathematical quantity. All five of these genetically evolved circuits constitute instances of an evolutionary computation technique solving a problem that is usually thought to require human intelligence.}, keywords = {genetic algorithms, genetic programming}, URL = {http://www.genetic-programming.com/jkpdf/idashcartwright2000.pdf}, author = { Koza, John R. and III, Forrest H Bennett and Andre, David and Keane, Martin A.} }