A Genetic Algorithm Approach to Exploring Simulation Parameters

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A Genetic Algorithm Approach to Exploring Simulation Parameters

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dc.contributor.author Ahmad, Saira
dc.date 2012-08-16
dc.date.accessioned 2012-09-14T14:22:24Z
dc.date.available 2012-09-14T14:22:24Z
dc.date.issued 2012-09-14
dc.identifier.uri http://hdl.handle.net/10214/3996
dc.description.abstract Simulation of animal disease spread is essential for understanding and controlling the outbreak of disease among herds of livestock (in particular cattle and poultry). Using a computerized system or simulator, animal health professionals or epidemiologists often spend many hours determining the set of input parameters that most accurately represent a disease spread or an outbreak scenario. A parameter can be a simple boolean value, or a scientific or often hypothetically derived range of real numbers. Many times, an epidemiologist chooses a value provisionally in a random fashion and repeats the simulation until a viable solution is achieved. This tedious process is inefficient and lengthy. To assist and improve this laborious practice in a concise and timely manner, a Genetic Algorithm is employed to determine a population based solution consisting of input parameters using the North American Animal Disease Spread Model (NAADSM). en_US
dc.language.iso en en_US
dc.rights.uri http://creativecommons.org/licenses/by/2.5/ca/ *
dc.subject Genetic Algorithm en_US
dc.subject animal disease spread simulator en_US
dc.subject stochastic simulation en_US
dc.subject simulation en_US
dc.subject simulation parameters en_US
dc.title A Genetic Algorithm Approach to Exploring Simulation Parameters en_US
dc.type Thesis en_US
dc.degree.programme Computer Science en_US
dc.degree.name Master of Science en_US
dc.degree.department Department of Computing and Information Science en_US

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