Using genetic algorithms to limit the optimism in Time Warp

It is well known that controlling the optimism in Time Warp is central to its success. To date, this problem has been approached by constructing a heuristic model of Time Warp's behavior and optimizing the models' performance. The extent to which the model actually reflects reality is therefore central to its ability to control Time Warp's behavior. In contrast to those approaches, using genetic algorithms avoids the need to construct models of Time Warp's behavior. We demonstrate, in this paper, how the choice of a time window for Time Warp can be transformed into a search problem, and how a genetic algorithm can be utilized to search for the optimal value of the window. An important quality of genetic algorithms is that they can start a search with a random choice for the values of the parameter(s) which they are trying to optimize and produce high quality solutions.

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