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Evolution using genetic programming of a lowdistortion 96 Decibel operational amplifier
 Proceedings of the 1997 ACM Symposium on Applied Computing
, 1997
"... 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 lowdistortio ..."
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Cited by 14 (7 self)
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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 lowdistortion 96 decibel (64,860to1) 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 userspecified 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 analogdigital circuitshas not proved to be as amenable to automation (Rutenbar 1993). In discussing "the analog dilemma, " O. Aaserud and I.
Discovery of Symbolic, NeuroSymbolic and Neural Networks with Parallel Distributed Genetic Programming
 In 3rd International Conference on Artificial Neural Networks and Genetic Algorithms, ICANNGA'97
, 1997
"... Parallel Distributed Genetic Programming (PDGP) is a new form of genetic programming suitable for the development of parallel programs in which symbolic and neural processing elements can be combined in a free and natural way. This paper describes the representation for programs and the genetic oper ..."
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Cited by 13 (8 self)
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Parallel Distributed Genetic Programming (PDGP) is a new form of genetic programming suitable for the development of parallel programs in which symbolic and neural processing elements can be combined in a free and natural way. This paper describes the representation for programs and the genetic operators on which PDGP is based. Experimental results on the XOR problem are also reported. 1
Selfreplicating and selfrepairing multicellular automata
 Artificial Life
, 1998
"... Biological organisms are among the most intricate structures known to man, exhibiting highly complex behavior through the massively parallel cooperation of numerous relatively simple elements, the cells. As the development of computing systems approaches levels of complexity such that their synthesi ..."
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Cited by 13 (7 self)
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Biological organisms are among the most intricate structures known to man, exhibiting highly complex behavior through the massively parallel cooperation of numerous relatively simple elements, the cells. As the development of computing systems approaches levels of complexity such that their synthesis begins to push the limits of human intelligence, engineers are starting to seek inspiration in nature for the design of computing systems, both at the software and at the hardware levels. This paper will present one such endeavor, notably an attempt to draw inspiration from biology in the design of a novel digital circuit: a fieldprogrammable gate array (FPGA). This reconfigurable logic circuit will be endowed with two features motivated and guided by the behavior of biological systems: selfreplication and selfrepair. 1
Automated synthesis of computational circuits using genetic programming
 Proceedings of the 1997 IEEE Conference on Evolutionary Computation. Piscataway, NJ
, 1997
"... 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 ..."
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Cited by 11 (4 self)
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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 problemspecific 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.
Use of ArchitectureAltering Operations to Dynamically Adapt a ThreeWay Analog Source Identification Circuit to Accommodate a New Source
"... The problem of source identification involves correctly classifying an incoming signal into a category that identifies the signal's source. The problem is ..."
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Cited by 7 (4 self)
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The problem of source identification involves correctly classifying an incoming signal into a category that identifies the signal's source. The problem is
A Genetic Programming Approach to Logic Function Synthesis by means of Multiplexers
 In
, 1999
"... This paper presents an approach based on the use of genetic programming to synthesize logic functions. The proposed approach uses the 1control line multiplexer as the only design unit, defining any logic function (defined by a truth table) through the replication of this single unit. Our fitness fu ..."
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Cited by 7 (0 self)
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This paper presents an approach based on the use of genetic programming to synthesize logic functions. The proposed approach uses the 1control line multiplexer as the only design unit, defining any logic function (defined by a truth table) through the replication of this single unit. Our fitness function first explores the search space trying to find a feasible design and then concentrates in the minimization of such (fully feasible) circuit. The proposed approach is illustrated using several sample Boolean functions.
Use of Evolutionary Techniques to Automate the Design of Combinational Circuits
, 1999
"... In this paper we propose an approach based on a genetic algorithm (GA) to design combinational logic circuits in which the objective is to minimize their total number of gates. Our results compare favorably against those produced by human designers and even another GAbased approach. We also briefly ..."
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Cited by 6 (4 self)
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In this paper we propose an approach based on a genetic algorithm (GA) to design combinational logic circuits in which the objective is to minimize their total number of gates. Our results compare favorably against those produced by human designers and even another GAbased approach. We also briefly analyze the solutions found by the GA trying to find some clues on how it reduces a Boolean expression, and we indicate that such a reduction is achieved by reusing common patterns within the circuit in ways that are sometimes completely nonintuitive for a human designer. However, in small circuits, these patterns can be easier to detect and our approach could, therefore, be useful to teach circuit design since it can show students what steps to follow to simplify further a certain solution.
Automatic programming of a timeoptimal robot controller and an analog electrical circuit to implement the robot controller by means of genetic programming
 Proceedings of 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Los Alamitos, CA; Computer Society Press. Pages 340
, 1997
"... 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 poi ..."
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Cited by 5 (3 self)
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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 timeoptimal 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 nearoptimal control strategy. 1.
Evolution of a timeoptimal flyto controller circuit using genetic programming
 Stanford University
, 1997
"... Most problemsolving 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 o ..."
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Cited by 4 (1 self)
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Most problemsolving 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 timeoptimal flyto control problem. 1.
Evolution of a LowDistortion, LowBias 60 Decibel Op Amp with Good Frequency Generalization using Genetic
 Programming, , presented at the International Conference on Evolvable System: From Biology to Hardware (ICES96
, 1996
"... Genetic programming was used to evolve both the topology and the sizing (numerical values) for each component of a lowdistortion, lowbias 60 decibel (1000to1) amplifier circuit with good frequency generalization. The evolved circuit was composed of two types of transistors (active elements) as we ..."
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Cited by 4 (1 self)
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Genetic programming was used to evolve both the topology and the sizing (numerical values) for each component of a lowdistortion, lowbias 60 decibel (1000to1) 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.