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Demonstration Of Circuit Design Using Randomness, Evolution And Natural Selection

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Conference

2006 Annual Conference & Exposition

Location

Chicago, Illinois

Publication Date

June 18, 2006

Start Date

June 18, 2006

End Date

June 21, 2006

ISSN

2153-5965

Conference Session

NEW Lab Experiments in Materials Science

Tagged Division

Materials

Page Count

8

Page Numbers

11.393.1 - 11.393.8

DOI

10.18260/1-2--234

Permanent URL

https://peer.asee.org/234

Download Count

191

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Paper Authors

biography

Glenn Kohne Loyola College in Maryland

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Glenn S. Kohne is currently associate professor of engineering science at Loyola College, Baltimore, MD. He received an M.E.S. from Loyola College in 1981 and a B.S.E.E. from the University of Maryland in 1970. His research interests include computer science, digital signal processing, and education.

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biography

Steven O'Donnell Loyola College in Maryland

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Mr. O’Donnell is a senior electrical engineering student at Loyola College in Maryland. He has studied abroad at Monash University in Melbourne Autralia. He has experience as a Hauber research grantee and as an intern at Middle River Aircraft Systems. His interests include skiing, waterskiing, fishing and traveling.

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Abstract
NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Demonstration of Circuit Design using Randomness, Evolution and Natural Selection Key Words: circuit design, analog filter, Darwinian circuit, evolutionary circuit, genetic circuit design, transfer function, iterative solution, student research project

Prerequisite Knowledge: Basic Linear Circuit Analysis and Windows Application programming.

Objective(s): 1. To explore the design of circuits using randomness and evolutionary principles; 2. To develop a tool for demonstrating the principles and for future research; 3. To demonstrate that people can create tools to perform design projects requiring knowledge more advanced than that held by the designer of the tools.

Equipment and Materials (include sources if appropriate): 1. A high-end PC with Windows 2000 (or better) 2. A compiler for Visual Basic 6.0 (or better) 3. MatrixVB (MATLAB product) 4. Access to an engineering reference library

(To use the tool developed by this project, only the PC is necessary)

Introduction: Most analog circuit design is inherently evolutionary in that the final product is achieved through an analytical analysis to determine parameter values, build a prototype, and test the prototype for “fitness”. Results of this testing frequently indicate changes in the original structure or parameter values, thereby causing the initial design to evolve into the final design. This loop generally begins with the selection of a circuit topography known to be a good candidate for the desired performance. The more experienced and skillful the designer, the more likely this first choice will resemble the final design. What follows becomes, essentially, a parameter optimization problem. Finding the optimal parameters manually, or even with the assistance of circuitry simulators is time and labor intensive. Tools available to help here include simulated annealing and gradient search methods. Shortcomings in these methods are the requirement of huge computational time and the difficulties of avoiding local maxima and minima.

Several broad categories of genetic evolution that have been applied to solve problems in computer programming, biology, chemistry, digital circuit design, and, to a lesser extent, analog circuit design. The tool developed here will work with both a variable structure and a variable number of parameters for components in each structure. The “brute-force” method of trying to optimize every circuit parameter against all the other circuit parameters for a fixed structure can require significant computation time. Trying to use brute-force on hundreds of different structures is simply impractical. The use of natural selection for fitness with genetic evolution of the fittest allows a relatively fast examination of a variety of structures as well as optimization of the element parameters. As with most engineering problem solving, this does not necessarily give the provably optimal circuit, but it does provide a means to design to a defined standard. It

Kohne, G., & O'Donnell, S. (2006, June), Demonstration Of Circuit Design Using Randomness, Evolution And Natural Selection Paper presented at 2006 Annual Conference & Exposition, Chicago, Illinois. 10.18260/1-2--234

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