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The Salmon Algorithm - A New Population Based Search Metaheuristic

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dc.contributor.author Orth, John
dc.date.accessioned 2012-03-02T14:16:28Z
dc.date.available 2012-03-02T14:16:28Z
dc.date.issued 2012-03-02
dc.identifier.uri http://hdl.handle.net/10464/3929
dc.description.abstract This thesis introduces the Salmon Algorithm, a search meta-heuristic which can be used for a variety of combinatorial optimization problems. This algorithm is loosely based on the path finding behaviour of salmon swimming upstream to spawn. There are a number of tunable parameters in the algorithm, so experiments were conducted to find the optimum parameter settings for different search spaces. The algorithm was tested on one instance of the Traveling Salesman Problem and found to have superior performance to an Ant Colony Algorithm and a Genetic Algorithm. It was then tested on three coding theory problems - optimal edit codes, optimal Hamming distance codes, and optimal covering codes. The algorithm produced improvements on the best known values for five of six of the test cases using edit codes. It matched the best known results on four out of seven of the Hamming codes as well as three out of three of the covering codes. The results suggest the Salmon Algorithm is competitive with established guided random search techniques, and may be superior in some search spaces. en_US
dc.language.iso eng en_US
dc.publisher Brock University en_US
dc.subject combinatorial optimization en_US
dc.subject coding theory en_US
dc.subject search metaheuristics en_US
dc.title The Salmon Algorithm - A New Population Based Search Metaheuristic en_US
dc.type Electronic Thesis or Dissertation en_US
dc.degree.name M.Sc. Computer Science en_US
dc.degree.level Masters en_US
dc.contributor.department Department of Computer Science en_US
dc.degree.discipline Faculty of Mathematics and Science en_US


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