Data exploration in evolutionary reconstruction of PET images
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Gray:2018:GPEM,
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author = "Cameron C. Gray and Shatha F. Al-Maliki and
Franck P. Vidal",
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title = "Data exploration in evolutionary reconstruction of
{PET} images",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2018",
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volume = "19",
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number = "3",
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pages = "391--419",
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month = sep,
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note = "Special issue on genetic programming, evolutionary
computation and visualization",
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keywords = "genetic algorithms, genetic programming, Parisian
Approach, Fly Algorithm, Tomography reconstruction,
Information visualisation, Data exploration, Artificial
evolution, Parisian evolution",
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ISSN = "1389-2576",
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URL = "https://doi.org/10.1007/s10710-018-9330-7",
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DOI = "doi:10.1007/s10710-018-9330-7",
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size = "29 pages",
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abstract = "This work is based on a cooperative co-evolution
algorithm called Fly Algorithm, which is an
evolutionary algorithm (EA) where individuals are
called flies. It is a specific case of the Parisian
Approach where the solution of an optimisation problem
is a set of individuals (e.g. the whole population)
instead of a single individual (the best one) as in
typical EAs. The optimisation problem considered here
is tomography reconstruction in positron emission
tomography (PET). It estimates the concentration of a
radioactive substance (called a radiotracer) within the
body. Tomography, in this context, is considered as a
difficult ill-posed inverse problem. The Fly Algorithm
aims at optimising the position of 3-D points that
mimic the radiotracer. At the end of the optimisation
process, the fly population is extracted as it
corresponds to an estimate of the radioactive
concentration. During the optimisation loop a lot of
data is generated by the algorithm, such as image
metrics, duration, and internal states. This data is
recorded in a log file that can be post-processed and
visualised. We propose using information visualisation
and user interaction techniques to explore the
algorithm's internal data. Our aim is to better
understand what happens during the evolutionary loop.
Using an example, we demonstrate that it is possible to
interactively discover when an early termination could
be triggered. It is implemented in a new stopping
criterion. It is tested on two other examples on which
it leads to a 60percent reduction of the number of
iterations without any loss of accuracy.",
- }
Genetic Programming entries for
Cameron C Gray
Shatha F Al-Maliki
Franck P Vidal
Citations