Automated extraction of damage features through genetic programming
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- @InProceedings{Harvey:2013:HMSBS,
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author = "Dustin Y. Harvey and Michael D. Todd",
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title = "Automated extraction of damage features through
genetic programming",
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booktitle = "Health Monitoring of Structural and Biological Systems
2013",
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year = "2013",
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editor = "Tribikram Kundu",
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volume = "8695",
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series = "Proceedings of SPIE",
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pages = "86950J-1--86950J-10",
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address = "San Diego, California, USA",
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month = "11 - 14 " # mar,
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publisher = "Society of Photo-Optical Instrumentation Engineers
(SPIE)",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1117/12.2009739",
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size = "10 pages",
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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
Citations