A comparison of novel stochastic optimization methods
Geiger, Peter C.
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Many real world problems are too complex to solve with traditional programming methods in a reasonable amount of time. Stochastic optimization techniques have been applied to this class of problems with success. Set up and tuning an algorithm can be a daunting task, so this thesis first presents a method of simple optimization requiring no tuning parameters. Then, methods for dealing with search spaces with invalid solution space are introduced and compared.