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A computational methodology is introduced to minimize infection opportunitiesfor people suffering some degree of lockdown in response to a pandemic, as isthe 2020 COVID-19 pandemic. Persons use their mobile phone or computationaldevice to request trips to places of their need or interest indicating a roughtime of day: `morning', `afternoon', `night' or `any time' when they would liketo undertake these outings as well as the desired place to visit. An artificialintelligence methodology which is a variant of Genetic Programming studies allrequests and responds with specific time allocations for such visits thatminimize the overall risks of infection, hospitalization and death of people. Anumber of alternatives for this computation are presented and results ofnumerical experiments involving over 230 people of various ages and backgroundhealth levels in over 1700 visits that take place over three consecutive days.A novel partial infection model is introduced to discuss these proof of conceptsolutions which are compared to round robin uninformed time scheduling forvisits to places. The computations indicate vast improvements with far fewerdead and hospitalized. These auger well for a more realistic study usingaccurate infection models with the view to test deployment in the real world.The input that drives the infection model is the degree of infection bytaxonomic class, such as the information that may arise from population testingfor COVID-19 or, alternatively, any contamination model. The taxonomy classassumed in the computations is the likely level of infection by age group.

Genetic Programming visitation scheduling solution can deliver a less austere COVID-19 pandemic population lockdown