Analyzing algorithms by simulation: variance reduction techniques and simulation speedups
ACM Computing Surveys (CSUR)
A calculus of mobile processes, I
Information and Computation
Design patterns: elements of reusable object-oriented software
Design patterns: elements of reusable object-oriented software
WSC' 90 Proceedings of the 22nd conference on Winter simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A Java-based simulation manager for web-based simulation
Proceedings of the 32nd conference on Winter simulation
Information Processing Letters
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Algorithm Selection using Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Proceedings of the 16th European Simulation Multiconference on Modelling and Simulation 2002
Algorithm selection for sorting and probabilistic inference: a machine learning-based approach
Algorithm selection for sorting and probabilistic inference: a machine learning-based approach
Concurrent Replication of Parallel and Distributed Simulations
Proceedings of the 19th Workshop on Principles of Advanced and Distributed Simulation
Statistical Models for Empirical Search-Based Performance Tuning
International Journal of High Performance Computing Applications
Learning dynamic algorithm portfolios
Annals of Mathematics and Artificial Intelligence
ANSS '07 Proceedings of the 40th Annual Simulation Symposium
The Black Swan: The Impact of the Highly Improbable
The Black Swan: The Impact of the Highly Improbable
An Algorithm Selection Approach for Simulation Systems
Proceedings of the 22nd Workshop on Principles of Advanced and Distributed Simulation
A Grid-Inspired Mechanism for Coarse-Grained Experiment Execution
DS-RT '08 Proceedings of the 2008 12th IEEE/ACM International Symposium on Distributed Simulation and Real-Time Applications
Stochastic kriging for simulation metamodeling
Proceedings of the 40th Conference on Winter Simulation
A flexible and scalable experimentation layer
Proceedings of the 40th Conference on Winter Simulation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
A portfolio approach to algorithm select
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Efficient, correct simulation of biological processes in the stochastic pi-calculus
CMSB'07 Proceedings of the 2007 international conference on Computational methods in systems biology
Regenerative systems: challenges and opportunities for modeling, simulation, and visualization
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
Automating the runtime performance evaluation of simulation algorithms
Winter Simulation Conference
Selecting Simulation Algorithm Portfolios by Genetic Algorithms
PADS '10 Proceedings of the 2010 IEEE Workshop on Principles of Advanced and Distributed Simulation
Comparing Parallel Simulation of Social Agents Using Cilk and OpenCL
DS-RT '11 Proceedings of the 2011 IEEE/ACM 15th International Symposium on Distributed Simulation and Real Time Applications
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Simulation replication is a necessity for all stochastic simulations. Its efficient execution is particularly important when additional techniques are used on top, such as optimization or sensitivity analysis. One way to improve replication efficiency is to ensure that the best configuration of the simulation system is used for execution. A selection of the best configuration is possible when the number of required replications is sufficiently high, even without any prior knowledge on simulator performance or problem instance. We present an adaptive replication mechanism that combines portfolio theory with reinforcement learning: it adapts itself to the given problem instance at runtime and can be restricted to an efficient algorithm portfolio.