The goal of getting computers to automatically solve problems is central to artificial intelligence, machine learning, and the broad area encompassed by what Turing called “machine intelligence ” [161, 162].

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

This paper describes an automated process for designing electrical circuits in which "What You Want Is What You Get " ("WYWIWYG " – pronounced "wow-eee-wig"). The design process uses genetic programming to produce both the topology of the desired circuit and the sizing (numerical values) for all the components of a circuit. Genetic programming successfully evolves both the topology and the sizing for an asymmetric bandpass filter that was described as being difficult-to-design in a leading electrical engineering journal. This evolved circuit is another instance in which a genetically evolved solution to a non-trivial problem is competitive with human performance. 1.

Abstract – It would be desirable if computers could solve problems without the need for a human to write the detailed programmatic steps. That is, it would be desirable to have a domain-independent automatic programming technique in which "What You Want Is What You Get " ("WYWIWYG " – pronounced "woweee-wig"). Genetic programming is such a technique. This paper surveys three recent examples of problems (from the fields of cellular automata and molecular biology) in which genetic programming evolved a computer program that produced results that were slightly better than human performance for the same problem. This paper then discusses the problem of electronic circuit synthesis in greater detail. It shows how genetic programming can evolve both the topology of a desired electrical circuit and the sizing (numerical values) for each component in a crossover (woofer and tweeter) filter. Genetic programming has also evolved the design for a lowpass filter, the design of an amplifier, and the design for an asymmetric bandpass filter that was described as being difficult-to-design in an article in a leading electrical engineering journal.

This paper describes the successful parallel implementation of genetic programming on a network of processing nodes using the transputer architecture. With this approach, researchers of genetic algorithms and genetic programming can acquire computing power that is intermediate between the power of currently available workstations and that of supercomputers at intermediate cost. This approach is illustrated by a comparison of the computational effort required to solve a benchmark problem. Because of the decoupled character of genetic programming, our approach achieved a nearly linear speed up from parallelization. In addition, for the best choice of parameters tested, the use of subpopulations delivered a super linear speed-up in terms of the ability of the algorithm to solve the problem. Several examples are also presented where the parallel genetic programming system evolved solutions that are competitive with human performance on the same problem.

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 architecturealtering operations provides a way to build complex structures with reused substructures. In this paper, we successfully evolved a design for a twoband 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.

This paper explores some of the issues involved in the use of evolutionary models

circuit synthesis, operational amplifier There is no known general technique for automatically designing an analog electrical circuit that satisfies design specifications. Genetic programming was used to evolve both the topology and the sizing (numerical values) for each component of a low-distortion 96 decibel (64,860-to-1) amplifier circuit. 1. THE ANALOG DILEMMA The field of engineering design offers a practical yardstick for evaluating automated techniques because the design process is usually viewed as requiring human intelligence and because design is a major activity of practicing engineers. In the design process, the design requirements specify "what needs to be done. " A satisfactory design tells us "how to do it." In the field of electrical engineering, the design process typically involves the creation of an electrical circuit that satisfies user-specified design goals. Considerable progress has been made in automating the design of certain categories of purely digital circuits; however, the design of analog circuits and mixed analog-digital circuitshas not proved to be as amenable to automation (Rutenbar 1993). In discussing "the analog dilemma, " O. Aaserud and I.

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 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. 1.

The problem of source identification involves correctly classifying an incoming signal into a category that identifies the signal's source. The problem is