- author = "James F. {Smith III}",
- title = "Evolutionary Data Mining of Digital Logic and the Effects of Uncertainty",
- booktitle = "2007 IEEE Congress on Evolutionary Computation",
- year = "2007",
- editor = "Dipti Srinivasan and Lipo Wang",
- pages = "39--46",
- address = "Singapore",
- month = "25-28 " # sep,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- ISBN = "1-4244-1340-0",
- file = "1179.pdf",
- keywords = "genetic algorithms, genetic programming",
-
DOI = "
doi:10.1109/CEC.2007.4424452",
- abstract = "A data mining based procedure for automated reverse engineering has been developed. The data mining algorithm for reverse engineering uses a genetic program (GP) as a data mining function. A genetic program is an algorithm based on the theory of evolution that automatically evolves populations of computer programs or mathematical expressions, eventually selecting one that is optimal in the sense it maximises a measure of effectiveness, referred to as a fitness function. The system to be reverse engineered is typically a sensor. Design documents for the sensor are not available and conditions prevent the sensor from being taken apart. The sensor is used to create a database of input signals and output measurements. Rules about the likely design properties of the sensor are collected from experts. The rules are used to create a fitness function for the genetic program. Genetic program based data mining is then conducted. This procedure incorporates not only the experts' rules into the fitness function, but also the information in the database. The information extracted through this process is the internal design specifications of the sensor. Significant experimental and theoretical results related to GP based data mining for reverse engineering and the related uncertainties will be provided.",
-
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
