Automated extraction of damage features through genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Harvey:2013:HMSBS,
  author =       "Dustin Y. Harvey and Michael D. Todd",
  title =        "Automated extraction of damage features through
                 genetic programming",
  booktitle =    "Health Monitoring of Structural and Biological Systems
                 2013",
  year =         "2013",
  editor =       "Tribikram Kundu",
  volume =       "8695",
  series =       "Proceedings of SPIE",
  pages =        "86950J-1--86950J-10",
  address =      "San Diego, California, USA",
  month =        "11 - 14 " # mar,
  publisher =    "Society of Photo-Optical Instrumentation Engineers
                 (SPIE)",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1117/12.2009739",
  size =         "10 pages",
  abstract =     "Robust damage detection algorithms are a fundamental
                 requirement for development of practical structural
                 health monitoring systems. Typically, structural
                 health-related decisions are made based on measurements
                 of structural response. Data analysis involves a
                 two-stage process of feature extraction and
                 classification. While classification methods are well
                 understood, feature design is difficult,
                 time-consuming, and requires application experts and
                 domain-specific knowledge. Genetic programming, a
                 method of evolutionary computing closely related to
                 genetic algorithms, has previously shown promise when
                 adapted to problems involving structured data such as
                 signals and images. Genetic programming evolves a
                 population of candidate solutions represented as
                 computer programs to perform a well-defined task.
                 Importantly, genetic programming conducts an efficient
                 search without specification of the size of the desired
                 solution. In this study, a novel formulation of genetic
                 programming is introduced as an automated feature
                 extractor for supervised learning problems related to
                 structural health monitoring applications. Performance
                 of the system is evaluated on signal processing
                 problems with known optimal solutions.",
}

Genetic Programming entries for Dustin Y Harvey Michael D Todd

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