Automated design of both the topology and sizing of analog electrical circuits using genetic programming   [AD] [GP]

by

Koza, J., R., Bennett III, F., H., Andre, D. and Keane, M., A.

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Info: Artificial Intelligence in Design '96 (Conference proceedings), 1996, p. 151-170
Keywords:genetic algorithms, genetic programming
Abstract:
This paper describes an automated process for designing analog electrical circuits based on the principles of natural selection, [NS] sexual recombination, and developmental biology. The design process starts with the random creation of a large population of program trees [PT] composed of circuit-constructing functions. Each program tree specifies the steps by which a fully developed circuit is to be progressively developed from a common embryonic circuit appropriate for the type of circuit that the user wishes to design. Each fully developed circuit is translated into a netlist, simulated using a modified version of SPICE, and evaluated as to how well it satisfies the user's design requirements. The fitness measure [FM] is a user-written computer program that may incorporate any calculable characteristic or combination of characteristics of the circuit, including the circuit's behavior in the time domain, its behavior in the frequency domain, its power consumption, the number [TNO] of components, cost of components, or surface area occupied by its components. The population of program trees [PT] is genetically bred over a series of many generations using genetic programming. Genetic programming [GP] [GP] is driven by a fitness measure [FM] and employs genetic operations such as Darwinian reproduction, sexual recombination (crossover), and occasional mutation to create offspring. This automated evolutionary process produces both the topology of the circuit and the numerical values for each component. This paper describes how genetic programming [GP] can evolve the circuit for a difficult-to-design low-pass filter.
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BibTex:
@InProceedings{koza:1996:adtsaec,
  author =       "John R. Koza and Forrest H {Bennett III} and David
                 Andre and Martin A Keane",
  title =        "Automated design of both the topology and sizing of
                 analog electrical circuits using genetic programming",
  booktitle =    "Artificial Intelligence in Design '96",
  year =         "1996",
  editor =       "John S. Gero and Fay Sudweeks",
  pages =        "151--170",
  address =      "Dordrecht",
  publisher =    "Kluwer Academic",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "This paper describes an automated process for
                 designing analog electrical circuits based on the
                 principles of natural selection, sexual recombination,
                 and developmental biology. The design process starts
                 with the random creation of a large population of
                 program trees composed of circuit-constructing
                 functions. Each program tree specifies the steps by
                 which a fully developed circuit is to be progressively
                 developed from a common embryonic circuit appropriate
                 for the type of circuit that the user wishes to design.
                 Each fully developed circuit is translated into a
                 netlist, simulated using a modified version of SPICE,
                 and evaluated as to how well it satisfies the user's
                 design requirements. The fitness measure is a
                 user-written computer program that may incorporate any
                 calculable characteristic or combination of
                 characteristics of the circuit, including the circuit's
                 behavior in the time domain, its behavior in the
                 frequency domain, its power consumption, the number of
                 components, cost of components, or surface area
                 occupied by its components. The population of program
                 trees is genetically bred over a series of many
                 generations using genetic programming. Genetic
                 programming is driven by a fitness measure and employs
                 genetic operations such as Darwinian reproduction,
                 sexual recombination (crossover), and occasional
                 mutation to create offspring. This automated
                 evolutionary process produces both the topology of the
                 circuit and the numerical values for each component.
                 This paper describes how genetic programming can evolve
                 the circuit for a difficult-to-design low-pass
                 filter.",
}