Evolutionary Calibration of Sensors using Genetic Programming on Evolvable Hardware

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Ho-Sik Seok and Byoung-Tak Zhang",
  title =        "Evolutionary Calibration of Sensors using Genetic
                 Programming on Evolvable Hardware",
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "630--634",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, sensor
                 calibration, evolvable hardware, mobile robot, Khepera,
                 autonomous mobile robots, complex dynamic environment,
                 decision-making ability, evolutionary calibration,
                 interpretation correctness, learning, noise,
                 reconfigurable sensor interpretation circuit, robot
                 perceptual ability, sensitivity, sensor calibration
                 logic, sensor reliability, calibration, control system
                 analysis computing, mobile robots, reconfigurable
                 architectures, reliability, sensitivity, sensors",
  ISBN =         "0-7803-6658-1",
  URL =          "http://bi.snu.ac.kr/Publications/Conferences/International/CEC2001_Seok.pdf",
  DOI =          "doi:10.1109/CEC.2001.934450",
  size =         "5 pages",
  abstract =     "In order to retain some degree of decision-making
                 ability in a complex and dynamic environment, there
                 have been many attempts to build autonomous mobile
                 robots. However, conventional methods pay little
                 attention to the unreliability of sensors. Because of
                 corruption by noise and differences in sensitivity,
                 even the same kinds of sensors show different
                 observations under the same conditions. This causes a
                 problem in that a minor change to the environment of
                 the sensor system has a great influence on the
                 perceptual ability of the robot. To improve the
                 reliability of the sensors, we present a method for the
                 evolutionary calibration of sensors using genetic
                 programming as the calibration mechanism. In our
                 approach, the sensor calibration logic is implemented
                 on evolvable hardware. Therefore, as the learning goes
                 on, the sensor interpretation circuit reconfigures
                 itself to a more suitable form at run-time. Through two
                 experiments on different tasks, we confirmed that our
                 method significantly improved the correctness of
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number = .

                 GP tree converted to bit string down loaded onto XC6216

Genetic Programming entries for Ho-Sik Seok Byoung-Tak Zhang