Forrest H. Bennett

Automated synthesis of analog electrical circuits by means of genetic programming

Analog circuit synthesis entails the creation of both the topology and the sizing (numerical values) of all of the circuits components. 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 low-pass filter, a crossover filter, a source identification circuit, an amplifier, a computational circuit, a time-optimal 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 of the number of inputs and outputs of the desired circuit, the types of available components, and a fitness measure that restates the high-level statement of the circuits 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.

Automatic Creation of Human-Competitive Programs and Controllers by Means of Genetic Programming

Genetic programming is an automatic method for creating a computer program or other complex structure to solve a problem. This paper first reviews various instances where genetic programming has previously produced human-competitive results. It then presents new human-competitive results involving the automatic synthesis of the design of both the parameter values (i.e., tuning) and the topology of controllers for two illustrative problems. Both genetically evolved controllers are better than controllers designed and published by experts in the field of control using the criteria established by the experts. One of these two controllers infringes on a previously issued patent. Other evolved controllers duplicate the functionality of other previously patented controllers. The results in this paper, in conjunction with previous results, reinforce the prediction that genetic programming is on the threshold of routinely producing human-competitive results and that genetic programming can potentially be used as an “invention machine” to produce patentable new inventions.

Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming

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. The fitness measure is a user-written computer program that may incorporate any calculable characteristic or combination of characteristics of the circuit. 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 as a Darwinian Invention Machine

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 two-part 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.

Automated synthesis of computational circuits using genetic programming

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.

Reuse, Parameterized Reuse, and Hierarchical Reuse of Substructures in Evolving Electrical Circuits Using Genetic Programming

Most practical electrical circuits contain modular substructures that are repeatedly used to create the overall circuit. Genetic programming with automatically defined functions and the recently developed architecture-altering operations provides a way to build complex structures with reused substructures. In this paper, we successfully evolved a design for a two-band crossover (woofer and tweeter) filter with a crossover frequency of 2,512 Hz. Both the topology and the sizing (numerical values) for each component of the circuit were evolved during the run. The evolved circuit contained three different noteworthy substructures. One substructure was invoked five times thereby illustrating reuse. A second substructure was invoked with different numerical arguments. This second substructure illustrates parameterized reuse because different numerical values were assigned to the components in the different instantiations of the substructure. A third substructure was invoked as part of a hierarchy, thereby illustrating hierarchical reuse.

Evolution of a 60 Decibel Op Amp Using Genetic Programming

Genetic programming was used to evolve both the topology and sizing (numerical values) for each component of a low-distortion, low-bias 60 decibel (1000-to-1) amplifier with good frequency generalization.

Synthesis of topology and sizing of analog electrical circuits by means of genetic programming

Abstract The design (synthesis) of an analog electrical circuit entails the creation of both the topology and sizing (numerical values) of all of the circuits components. There has previously been no general automated technique for automatically creating the design for an analog electrical circuit from a high-level statement of the circuits desired behavior. This paper shows how genetic programming can be used to automate the design of eight prototypical analog circuits, including a lowpass filter, a highpass filter, a bandstop filter, a tri-state frequency discriminator circuit, a frequency-measuring circuit, a 60 dB amplifier, a computational circuit for the square root function, and a time-optimal robot controller circuit.

Design of decentralized controllers for self-reconfigurable modular robots using genetic programming

Advantages of self-reconfigurable modular robots over conventional robots include physical adaptability, robustness in the presence of failures, and economies of scale. Creating control software for modular robots is one of the central challenges to realizing their potential advantages. Modular robots differ enough from traditional robots that new techniques must be found to create software to control them. The novel difficulties are due to the fact that modular robots are ideally controlled in a decentralized manner, dynamically change their connectivity topology, may contain hundreds or thousands of modules, and are expected to perform tasks properly even when some modules fail. We demonstrate the use of genetic programming to automatically create distributed controllers for self-reconfigurable modular robots.

Automatic creation of both the topology and parameters for a robust controller by means of genetic programming

The paper describes a general automated method for synthesizing the design of both the topology and parameter values for controllers. The automated method automatically makes decisions concerning the total number of processing blocks to be employed in the controller, the type of each block, the topological interconnections between the blocks, the values of all parameters for the blocks, and the existence, if any, of internal feedback between the blocks of the overall controller. Incorporation of time-domain, frequency-domain, and other constraints on the control or state variables (often analytically intractable using conventional methods) can be readily accommodated. The automatic method described in the paper (genetic programming) is applied to a problem of synthesizing the design of a robust controller for a plant with a second-order lag. A textbook PID compensator preceded by a lowpass pre-filter delivers credible performance on this problem. However, the automatically created controller employs a second derivative processing block (in addition to proportional, integrative, and derivative blocks and a pre-filter). It is better than twice as effective as the textbook controller as measured by the integral of the time-weighted absolute error, has only two-thirds of the rise time in response to the reference (command) input, and is 10 times better in terms of suppressing the effects of disturbance at the plant input.