s: Jan. 1995 { Sep. 1998 ACM: ACM Guide to Computing Literature: 1979 - 1993/4 BA: Biological Abstracts: July 1996 - Aug. 1998 CA: Computer Abstracts: Jan. 1993 { Feb. 1995 CCA: Computer & Control Abstracts: Jan. 1992 { Apr. 1998 (except May-95) ChA: Chemical Abstracts: Jan. 1997 - Dec. 1998 CTI: Current Technology Index Jan./Feb. 1993 { Jan./Feb. 1994 DAI: Dissertation Abstracts International: Vol. 53 No. 1 { Vol. 56 No. 10 (Apr. 1996) EEA: Electrical & Electronics Abstracts: Jan. 1991 { Apr. 1998 EI A: The Engineering Index Annual: 1987 - 1992 EI M: The Engineering Index Monthly: Jan. 1993 { Apr. 1998 (except May 1997) N: Scientic and Technical Aerospace Reports: Jan. 1993 - Dec. 1995 (except Oct. 1995) P: Index to Scientic & Technical Proceedings: Jan. 1986 { May 1998 (except Nov. 1994) PA: Physics Abstracts: Jan. 1997 { Sep. 1998 1.1 Your contributions erroneous or missing? The bibliography database is updated on a regular basis and certain...

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

In the field of Operation Research and Artificial Intelligence, several stochastic search algorithms have been designed based on the theory of global random search (Zhigljavsky 1991). Basically, those techniques iteratively sample the search space with respect to a probability distribution which is updated according to the result of previous samples and some predefined strategy. Genetic Algorithms (GAs) (Goldberg 1989) or Greedy Randomized Adaptive Search Procedures (GRASP) (Feo & Resende 1995) are two particular instances of this paradigm. In this paper, we present SAGE, a search algorithm based on the same fundamental mechanisms as those techniques. However, it addresses a class of problems for which it is difficult to design transformation operators to perform local search because of intrinsic constraints in the definition of the problem itself. For those problems, a procedural approach is the natural way to construct solutions, resulting in a state space represented as a tree or a ...

This paper demonstrates that a design for a low-distortion highgain 96 decibel (64,860-to-1) operational amplifier (including both circuit topology and component sizing) can be evolved using genetic programming.

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.

Genetic programming was used to evolve both the topology and the sizing (numerical values) for each component of a low-distortion, lowbias 60 decibel (1000-to-1) amplifier circuit with good frequency generalization. The evolved circuit was composed of two types of transistors (active elements) as well as resistors and capacitors. 1. Introduction The problem of circuit synthesis involves designing an electrical circuit that satisfies user-specified design goals. A complete design of an electrical circuit includes both its topology and the sizing of all its components. The topology of a circuit consists of the number of components in the circuit, the type of each component, and a list of all the connections between the components. The sizing of a circuit consists of the component value(s) of each component. Evolvable hardware is one approach to automated circuit synthesis. Some of the early pioneering work in this field includes that of Higuchi, Niwa, Tanaka, Iba, de Garis, and Furuya...

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.

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 "wow-eee-wig"). Genetic programming is such a technique. This paper surveys three recent examples of problems (one from the field of cellular automata and two from the fields of 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 a fourth problem in greater detail and demonstrates that a design for a low-distortion 96 decibel op amp (including both topology and component sizing) can be evolved using genetic programming. The information that the user must supply to genetic programming consists of the parts bin (transistors, resistors, and capacitors) and the fitness m...

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