Optimization of test engineering utilizing evolutionary computation

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Joseph Engler",
  title =        "Optimization of test engineering utilizing
                 evolutionary computation",
  booktitle =    "IEEE AUTOTESTCON, 2009",
  year =         "2009",
  month =        sep,
  pages =        "447--452",
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 adaptive memory, automated station software generation,
                 evolutionary computation, genetic programming
                 algorithm, test engineering optimization, test station
                 software creation, testing requirements, automatic test
                 pattern generation, automatic test software",
  DOI =          "doi:10.1109/AUTEST.2009.5314025",
  ISSN =         "1088-7725",
  abstract =     "Test engineering often experiences pressures to
                 produce test stations and software in a short time
                 frame with constrained budgets. Since test is a
                 negative influence towards product costs, it is crucial
                 to optimize the processes of test station software
                 creation as well as the configuration of the test
                 station itself. This paper introduces novel
                 methodologies for optimized station configuration and
                 automated station software generation. These two
                 optimizations use evolutionary computation to
                 automatically generate software for the test station
                 and to offer optimal configurations of the station
                 based upon testing requirements. Presented is a
                 modified genetic programming algorithm for the creation
                 of test station software (e.g. COTS software drivers).
                 The genetic algorithm is improved through use of
                 adaptive memory to recall historic schemas of high
                 fitness. From the automated software generation an
                 optimal station configuration is produced based upon
                 the requirements of the testing to be performed. This
                 system has been implemented in industry and an actual
                 industrial case study is presented to illustrate the
                 efficiency of this novel optimization technique.
                 Comparisons with standard genetic programming
                 techniques are offered to further illustrate the
                 efficiency of this methodology.",
  notes =        "Also known as \cite{5314025}",

Genetic Programming entries for Joseph Engler