Genetic Algorithms use life as their model to solve difficult problems in computer science. They use a collection of possible solutions to find an optimal solution in extremely large search spaces. They follow biological processes such as crossover and mutation and implement Darwinian natural selection to use the better solutions to create more possible solutions until an optimal solution has been found. The Traveling Salesperson Problem lies within a class of problems called NP. These problems are not solvable in a reasonable amount of time using classical methods. Genetic Algorithms are an alternative method for attempting to solve such problems. This paper explores a specific Genetic Algorithm package named,""DGENESIS"" and how it is used to solve the Traveling Salesperson Problem. We tested several variables within DGENESIS to gain an understanding of how they could be adjusted to improve performance at solving the Traveling Salesperson Problem.
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Criswell, Michael, "Using Genetic Algorithms to Solve the Geometric Traveling Salesperson Problem" (1996). Honors Theses. 745.