language: en

Use of Architecture-Altering Operations to Dynamically Adapt a Three-Way Analog Source Identification Circuit to Accommodate a New Source

John R. Koza
Computer Science Dept. Stanford University
Stanford, California 94305-9020
koza@cs.stanford.edu
Forrest H Bennett III
Visiting Scholar Computer Science Dept. Stanford University
Stanford, California 94305
forrest@evolute.com
Jason Lohn
Visiting Scholar Computer Science Dept. Stanford University
Stanford, California 94305
jlohn7@leland.stanford.edu
Frank Dunlap
Dunlap Consulting
Palo Alto, California
Martin A. Keane
Martin Keane Inc.
5733 West Grover Chicago, Illinois 60630
makeane@ix.netcom.com
David Andre
Computer Science Division University of California
Berkeley, California
dandre@cs.berkeley.edu

ABSTRACT The problem of source identification involves correctly classifying an incoming signal into a category that identifies the signal's source. The problem is difficult because information is not provided distinguishing characteristics and because successive signals from the same source differ. The source identification problem can be made more difficult by dynamically changing the repertoire of sources while the problem is being solved. We used genetic programming to evolve both the topology and the sizing (numerical values) for each component of an analog electrical circuit that can correctly classify an incoming analog electrical signal into three categories. Then, the repertoire of sources was dynamically changed by adding a new source during the run. The paper describe show the enabled genetic programming to adapt, during the run, to the changed environment. Specifically, a three-way source identification circuit was evolved and then adapted into a four-way classifier, during the run, thereby successfully handling the additional new source.

1. Introduction