ACM Transactions on Programming Languages and Systems (TOPLAS)
An analysis of rollback-based simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Selecting the checkpoint interval in time warp simulation
PADS '93 Proceedings of the seventh workshop on Parallel and distributed simulation
Probabilistic adaptive direct optimism control in Time Warp
PADS '95 Proceedings of the ninth workshop on Parallel and distributed simulation
Adaptive flow control in time warp
Proceedings of the eleventh workshop on Parallel and distributed simulation
A spectrum of options for parallel simulation
WSC '88 Proceedings of the 20th conference on Winter simulation
Parallel and Distribution Simulation Systems
Parallel and Distribution Simulation Systems
An Introduction to Genetic Algorithms for Scientists and Engineers
An Introduction to Genetic Algorithms for Scientists and Engineers
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Optimizing time warp simulation with reinforcement learning techniques
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
Selecting Simulation Algorithm Portfolios by Genetic Algorithms
PADS '10 Proceedings of the 2010 IEEE Workshop on Principles of Advanced and Distributed Simulation
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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.