Evolving Finite State Machines with Embedded Genetic Programming for Automatic Target Detection within SAR Imagery

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{benson:2000:efsmegpatdsi,
  author =       "Karl A Benson",
  title =        "Evolving Finite State Machines with Embedded Genetic
                 Programming for Automatic Target Detection within SAR
                 Imagery",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "1543--1549",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, image
                 processing applications, Kohonen neural networks,
                 automatic target detection, control structure, embedded
                 genetic programming, evolving finite state machines,
                 node cardinality, symbiotic relationship, embedded
                 systems, finite state machines, object detection,
                 self-organising feature maps",
  ISBN =         "0-7803-6375-2",
  DOI =          "doi:10.1109/CEC.2000.870838",
  abstract =     "This paper presents a model comprising Finite State
                 Machines (FSMs) with embedded Genetic Programs (GPs)
                 which co-evolve to perform the task of Automatic Target
                 Detection (ATD). The fusion of a FSM and GPs allows for
                 a control structure (main program), the FSM, and
                 sub-programs, the GPs, to co-evolve in a symbiotic
                 relationship. The GP outputs along with the FSM state
                 transition levels are used to construct confidence
                 intervals that enable each pixel within the image to be
                 classified as either target or non-target, or to cause
                 a state transition to take place and further analysis
                 of the pixel to be performed. The algorithms produced
                 using this method consist of nominally four GPs, with a
                 typical node cardinality of less than ten, that are
                 executed in an order dictated by the FSM. The results
                 of the experimentation performed are compared to those
                 obtained in two independent studies of the same problem
                 using Kohonen Neural Networks and a two stage Genetic
                 Programming strategy.",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644

                 ",
}

Genetic Programming entries for Karl A Benson

Citations