No abstract
-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 high-level 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 temperature-sensing circuit, and a voltage reference 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 highlevel statement of the circuit's desired behavior as a measurable mathematical quantity.The eight genetically evolved circuits constitute an instance of an evolutionary computation technique producing results on a task that is usually thought of as requiring human intelligence. The fact that a single uniform approach yielded a satisfactory design for each of the eight circuits as well as the fact that a satisfactory design was created on the first or second run of each problem are evidence for the general applicability of genetic programming for solving the problem of automatic synthesis of analog electrical circuits.
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 problem-specific 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.
Genetic programming is known to be capable of creating designs that satisfy prespecified high-level design requirements for analog electrical circuits and other complex structures. However, in the real world, it is often important that a design satisfy various non-technical requirements. One such requirement is that a design not possess the key characteristics of any previously known design. This paper shows that genetic programming can be used to generate novel solutions to a design problem so that genetic programming may be potentially used as an invention machine. This paper turns the clock back to the period just before the time (1917) when George Campbell of American Telephone and Telegraph invented and patented the design for an electrical circuit that is now known as the ladder filter. Genetic programming is used to reinvent the Campbell filter. The paper then turns the clock back to the period just before the time (1928) when Wilhelm Cauer invented and patented the elliptic filter. Genetic programming is then used to reinvent a technically equivalent filter that avoids the key characteristics of then-preexisting Campbell filter. Genetic programming can be used as an invention machine by employing a twopart fitness measure that incorporates both the degree to which an individual in the population satisfies the given technical requirements and the degree to which the individual does not possess the key characteristics of preexisting technology.
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