Automated design of both the topology and sizing of analog electrical circuits using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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",
• URL = "http://www.genetic-programming.com/jkpdf/aid1996.pdf",
• DOI = "doi:10.1007/978-94-009-0279-4_9",
• 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.",

}

Genetic Programming entries for John Koza Forrest Bennett David Andre Martin A Keane

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