- author = "Daniel Howard",
- title = "Genetic Programming visitation scheduling in lockdown with partial infection model that leverages information from {COVID-19} testing",
- institution = "ITLab, Inha University",
- year = "2020",
- number = "ITLAB-TR-2020-02",
- address = "Room 1301, HITECH Building, 100, Inha-ro, Nam-gu, Incheon, South Korea",
- month = "3 " # jun,
- keywords = "genetic algorithms, genetic programming, Software as a Service, SaaS, Corona pandemic",
- broken = "http://itlab.inha.ac.kr/#tr",
- broken = "https://drive.google.com/file/d/1u9Ti5p7w_4-uA2o3l-CTslnSLCdlxQxO/view",
- broken = "https://www.human-competitive.org/sites/default/files/howard_0.txt",
-
URL = "
https://www.human-competitive.org/sites/default/files/replacementhoward_0.pdf",
- broken = "http://www.human-competitive.org/sites/default/files/humies2020_d_howard_entry.mp4",
- size = "37 pages",
- abstract = "This report introduces a computational methodology to minimize infection opportunities for people suffering some degree of lockdown in response to a pandemic. Persons use their mobile phone or computational device to request trips to places of need or of their interest. An artificial intelligence methodology which uses Genetic Programming studies all requests and responds with granted 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 as well as the results of numerical experiments involving over 200 people of various ages. In particular, a model of partial infection is developed and implemented to address the real world situation whereby COVID-19 testing indicates risks of infection for members of a taxonomic class - for example, age groups, exploiting such information for the aforementioned purpose.",
-
notes = "See also \cite{howard2020genetic}
2020 HUMIES finalist.",
