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

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

An evolutionary algorithm is used to evolve a digital circuit which computes a simple hash function mapping a 16-bit address space into an 8-bit one. The target technology is FPGA, where the search space of the algorithm is made of the combinational functions computed by cells and of the interconnections among cells. An experimental study is carried out to determine the best set of parameters for on-line execution. It is observed that small population size leads to more effective results when short execution time is required. An application of the evolutionary approach presented in the paper for on-line tuning of the function during cache memory operation is also discussed.

An evolutionary algorithm is used to evolve a digital circuit which computes a simple hash function mapping a 16 bit address space into an 8 bit one. This circuit, based on FPGAs, is readily applicable to the design of set-associative cache memories. Possible use the evolutionary approach presented in the paper for on-line tuning of the function during cache operation is also discussed.

Genetic programming is an automatic programming technique that evolves computer programs to solve, or approximately solve, problems. This paper presents two examples in which genetic programming creates a computer program for controlling a robot so that the robot moves to a specified destination point in minimal time. In the first approach, genetic programming evolves a computer program composed of ordinary arithmetic operations and conditional operations to implement a time-optimal control strategy. In the second approach, genetic programming evolves the design of an analog electrical circuit consisting of transistors, diodes, resistors, and power supplies to implement a near-optimal control strategy. 1.

Most problem-solving techniques used by engineers involve the introduction of analytical and mathematical representations and techniques that are entirely foreign to the problem at hand. Genetic programming offers the possibility of solving problems in a more direct way using the given ingredients of the problem. This idea is explored by considering the problem of designing an electrical controller to implement a solution to the time-optimal fly-to control problem. 1.

The design (synthesis) of analog electrical circuits entails the creation of both the topology and sizing (numerical values) of all of the circuit's components. There has previously been no general automated technique for automatically designing an analog electrical circuit from a high-level statement of the circuit's desired behavior. This paper shows how genetic programming can be used to automate the design of both the topology and sizing of a suite of five prototypical analog circuits, including a lowpass filter, a tri-state frequency discriminator circuit, a 60 dB amplifier, a computational circuit for the square root, and a timeoptimal robot controller circuit. All five of these genetically evolved circuits constitute instances of an evolutionary computation technique solving a problem that is usually thought to require human intelligence.

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this paper refers to 14 instances where genetic programming has produced results that are competitive with those produced by human engineers, designers, mathematicians, or programmers

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