Evolutionary Data Mining Approach to Creating Digital Logic. Smith, III, J. & Nguyen, T. In International Conference on Informatics in Control Automation and Robotics (ICINCO), pages 107-113, 2006. Springer. ![Evolutionary Data Mining Approach to Creating Digital Logic [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex 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 maximizes 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 mathematical formalism and experimental results related to GP based data mining for reverse engineering will be provided.
@inproceedings{edmacdl,
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 maximizes 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 mathematical formalism and experimental results related to GP based data mining for reverse engineering will be provided.},
Author = {{Smith, III}, James and Nguyen, ThanhVu},
Booktitle = {International Conference on Informatics in Control Automation and Robotics (ICINCO)},
Keywords = {optimization problems in signal processing, signal reconstruction, system identification, time series and system modeling},
Pages = {107-113},
Publisher = {Springer},
Title = {{Evolutionary Data Mining Approach to Creating Digital Logic}},
Url_paper = {Pub/incinco06dm6_wv54.pdf},
Year = {2006}}
Downloads: 0
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