Automated design of both the topology and sizing of analog electrical circuits using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @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
Citations