Application and Evaluation of Genetic Programming for Aimpoint Selection

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{schwartz:1996:aim,
  author =       "Carey Schwartz and Charles Keyes and 
                 E. van Bronkhorst",
  title =        "Application and Evaluation of Genetic Programming for
                 Aimpoint Selection",
  booktitle =    "Adaptive Computing: Mathematical and Physical Methods
                 for Complex Environments",
  year =         "1996",
  editor =       "H. John Caulfield and Su-Shing Chen",
  volume =       "2824",
  pages =        "191--200",
  address =      "Denver",
  month =        "4--5 " # aug,
  organisation = "SPIE",
  keywords =     "genetic algorithms, genetic programming, ANN, ID3,
                 C4.5",
  URL =          "http://spie.org/x648.html?product_id=258132",
  abstract =     "We report on the application of genetic programming to
                 the determination of a desired output vector from an
                 input vector. Genetic programming is an emerging
                 technique similar in spirit to genetic algorithms which
                 employ a metric to drive a parallel search of the
                 solution space. In contrast to genetic algorithms which
                 yield a single encoded string as the solution, genetic
                 programming yields a computer program which can be
                 examined and understood. Genetic programming also
                 offers the possibility of enabling a technique whereby
                 feature vectors can be automatically developed. We have
                 applied the technique to the determination of ship
                 aimpoints from segmented imagery using input from a
                 sensor. The raw imagery is then processed and a feature
                 vector extracted, as was done in a previous problem.
                 The feature vectors are then used as input to the
                 genetic programming technique. We will report on the
                 sensitivity of performance of the genetic programming
                 technique as a function of the metric employed. In
                 addition we will compare the performance of the
                 computer program obtained by genetic programming to the
                 performance of a back propagation neural networks
                 developed for our problem. Furthermore we will report
                 on the performance results obtained using genetic
                 programming with and without the presence of
                 automatically created subroutines as well as the
                 determination of critical inputs.",
  notes =        "[2824-30] Naval Air Warfare Centre, Chinalake, CA,
                 USA

                 Chip Keyes.

                 Compares three layer feedforward ANN with
                 backpropergation, C4.5 (a derivative of ID3) and GP on
                 a X-Y learning of 192 examples with 20 inputs on each.
                 No requirement for out-of-sample generalisation. Some
                 ANN and C4.5 were able to learn them all but {"}the
                 C4.5 solutions appear incapable of generalizing{"} (in
                 the presence of noise) and {"}The GP approach is
                 unsatisfactory for this application{"}. [p199]

                 See also ANNIE
                 1996

                 http://web.umr.edu/~annie/annie96/fnl/node44.html

                 TA2.3 EVOLUTIONARY PROGRAMMING IV, Senator

                 Application and Evaluation of Genetic Programming for
                 Aimpoint Selection Carey Schwartz, Charles Keyes, and
                 Erik van Bronkhorst, Naval Air Warfare Center Weapons
                 Division, China Lake, CA",
}

Genetic Programming entries for Carey Schwartz Charles Keyes E van Bronkhorst