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Automated Synthesis of Analog Electrical Circuits by Means of Genetic Programming
, 1997
"... The design (synthesis) of analog electrical circuits starts with a highlevel statement of the circuit's desired behavior and requires creating a circuit that satisfies the specified design goals. Analog circuit synthesis entails the creation of both the topology and the sizing (numerical values) of ..."
Abstract

Cited by 64 (8 self)
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The design (synthesis) of analog electrical circuits starts with a highlevel statement of the circuit's desired behavior and requires creating a circuit that satisfies the specified design goals. Analog circuit synthesis entails the creation of both the topology and the sizing (numerical values) of all of the circuit's components. The difficulty of the problem of analog circuit synthesis is well known and there is no previously known general automated technique for synthesizing an analog circuit from a highlevel statement of the circuit's desired behavior. This paper presents a single uniform approach using genetic programming for the automatic synthesis of both the topology and sizing of a suite of eight different prototypical analog circuits, including a lowpass filter, a crossover (woofer and tweeter) filter, a source identification circuit, an amplifier, a computational circuit, a timeoptimal controller circuit, a temperaturesensing circuit, and a voltage reference circuit. The problemspecific 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 highlevel
Automated synthesis of computational circuits using genetic programming
 Proceedings of the 1997 IEEE Conference on Evolutionary Computation. Piscataway, NJ
, 1997
"... Abstract: Analog electrical circuits that perform mathematical functions (e.g., cube root, square) are called computational circuits. Computational circuits are of special practical importance when the small number of required mathematical functions does not warrant converting an analog signal into ..."
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Cited by 11 (4 self)
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Abstract: Analog electrical circuits that perform mathematical functions (e.g., cube root, square) are called computational circuits. Computational circuits are of special practical importance when the small number of required mathematical functions does not warrant converting an analog signal into a digital signal, performing the mathematical function in the digital domain, and then converting the result back to the analog domain. The design of computational circuits is difficult even for mundane mathematical functions and often relies on the clever exploitation of some aspect of the underlying device physics of the components. Moreover, implementation of each different mathematical function typically requires an entirely different clever insight. This paper demonstrates that computational circuits can be designed without such problemspecific insights using a single uniform approach involving genetic programming. Both the circuit topology and the sizing of all circuit components are created by genetic programming. This uniform approach to the automated synthesis of computational circuits is illustrated by evolving circuits that perform the cube root function (for which no circuit was found in the published literature) as well as for the square root, square, and cube functions. 1.
Iterative Refinement of Computational Circuits Using Genetic Programming
 IEEE POSIX. IEEE POSIX 1003.1c Threads API
, 2002
"... Previous work has shown that genetic programming is capable of creating analog electrical circuits whose output equals common mathematical functions, merely by specifying the desired mathematical function that is to be produced. This paper extends this work by generating computational circuits ..."
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Cited by 3 (0 self)
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Previous work has shown that genetic programming is capable of creating analog electrical circuits whose output equals common mathematical functions, merely by specifying the desired mathematical function that is to be produced. This paper extends this work by generating computational circuits whose output is an approximation to the error function associated with an existing computational circuit (created by means of genetic programming or some other method). The output of the evolved circuit can then be added to the output of the existing circuit to produce a circuit that computes the desired function with greater accuracy. This process can be performed iteratively. We present a set of results showing the effectiveness of this approach over multiple iterations for generating squaring, square root, and cubing computational circuits. We also perform iterative refinement on a recently patented cubic signal generator circuit, obtaining a refined circuit that is 7.2 times more accurate than the original patented circuit. The iterative refinement process described herein can be viewed as a method for using previous knowledge (i.e. the existing circuit) to obtain an improved result.
Use of TimeDomain Simulations in Automatic Synthesis of Computational Circuits Using Genetic Programming
 Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference. Las Vegas, NV
, 2000
"... Previously reported applications of genetic programming to the automatic synthesis of computational circuits have employed simulations based on DC sweeps. DC sweeps have the advantage of being considerably less timeconsuming than timedomain simulations. However, this type of simulation does ..."
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Cited by 1 (1 self)
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Previously reported applications of genetic programming to the automatic synthesis of computational circuits have employed simulations based on DC sweeps. DC sweeps have the advantage of being considerably less timeconsuming than timedomain simulations. However, this type of simulation does not necessarily lead to robust circuits that correctly perform the desired mathematical function over time. This paper addresses the problem of automatically synthesizing computational circuits using multiple timedomain simulations and presents results involving the synthesis of both the topology and sizing for a squaring, square root, and multiplier computational circuit and a lag circuit (from the field of control).
Computer Science Dept.
 Proceedings of Joint Conference of Information Sciences
, 1997
"... Automated synthesis of analog electronic circuits is recognized as a difficult problem. Genetic programming was used to evolve both the topology and the sizing (numerical values) for each component of a circuit that can perform source identification by correctly classify an incoming signal ..."
Abstract
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Automated synthesis of analog electronic circuits is recognized as a difficult problem. Genetic programming was used to evolve both the topology and the sizing (numerical values) for each component of a circuit that can perform source identification by correctly classify an incoming signal into categories.