Genetic Programming visitation scheduling solution can deliver a less austere COVID-19 pandemic population lockdown
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Misc{howard2020genetic,
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author = "Daniel Howard",
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title = "Genetic Programming visitation scheduling solution can
deliver a less austere {COVID-19} pandemic population
lockdown",
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howpublished = "arXiv",
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year = "2020",
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month = "22 " # jun,
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keywords = "genetic algorithms, genetic programming",
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URL = "https://arxiv.org/abs/2006.10748",
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broken = "http://www.human-competitive.org/sites/default/files/humies2020_d_howard_entry.mp4",
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eprint = "2006.10748",
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archiveprefix = "arXiv",
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primaryclass = "cs.NE",
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size = "41 pages",
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abstract = "A computational methodology is introduced to minimize
infection opportunities for people suffering some
degree of lockdown in response to a pandemic, as is the
2020 COVID-19 pandemic. Persons use their mobile phone
or computational device to request trips to places of
their need or interest indicating a rough time of day:
morning, afternoon, night or any time when they would
like to undertake these outings as well as the desired
place to visit. An artificial intelligence methodology
which is a variant of Genetic Programming studies all
requests and responds with specific time allocations
for such visits that minimize the overall risks of
infection, hospitalization and death of people. A
number of alternatives for this computation are
presented and results of numerical experiments
involving over 230 people of various ages and
background health levels in over 1700 visits that take
place over three consecutive days. A novel partial
infection model is introduced to discuss these proof of
concept solutions which are compared to round robin
uninformed time scheduling for visits to places. The
computations indicate vast improvements with far fewer
dead and hospitalized. These auger well for a more
realistic study using accurate infection models with
the view to test deployment in the real world. The
input that drives the infection model is the degree of
infection by taxonomic class, such as the information
that may arise from population testing for COVID-19 or,
alternatively, any contamination model. The taxonomy
class assumed in the computations is the likely level
of infection by age group.",
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notes = "See also \cite{ITLAB-TR-2020-02} 2020 HUMIES
finalist.",
- }
Genetic Programming entries for
Daniel Howard
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