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Efficient Multi-Start Strategies for Local Search Algorithms
7 references·1 citing·22 second hop
Efficient Multi-Start Strategies for Local Search Algorithms (2011)Efficient Multi-Start Strategies for Loca…Experimental Research in Evolutionary Computation: The New Experimentalism (2010)Experimental Research in Evol…Learning dynamic algorithm portfolios (2007)Learning dynamic algorithm po…An asymptotically optimal algorithm for the max k-armed bandit problem (2006)An asymptotically optimal alg…The max K-armed bandit: a new model of exploration applied to search heuristic selection (2005)The max K-armed bandit: a new…Finite-time Analysis of the Multiarmed Bandit Problem (2002)Finite-time Analysis of the M…Implementation of the simultaneous perturbation algorithm for stochastic optimization (1998)Implementation of the simulta…Multivariate stochastic approximation using a simultaneous perturbation gradient approximation (1992)Multivariate stochastic appro…Bandits attack function optimization (2014)Bandits attack function optim…An Introduction to Statistical Modeling of Extreme Values (2001)The Nonstochastic Multiarmed Bandit Problem (2002)ε-first policies for budget-limited multi-armed bandits (2010)Bandit Problems, Sequential Allocation of Experiments. (1987)Algorithm Selection as a Bandit Problem with Unbounded Losses (2010)Bandits Games and Clustering Foundations (2010)Some aspects of the sequential design of experiments (1952)Gambling in a rigged casino: The adversarial multi-armed bandit problem (2002)An algorithm portfolio for the dynamic maximal covering location problem (2016)Best Arm Identification in Multi-Armed Bandits (2010)Stochastic Estimation of the Maximum of a Regression Function (1952)Feature Article: Optimization for simulation: Theory vs. Practice (2002)Simulation optimization: a review of algorithms and applications (2015)Optimization of simulated systems: OptQuest and alternatives (2007)Response surface methodology for constrained simulation optimization: An overview (2007)Robust optimization – A comprehensive survey (2007)An Overview of the Simultaneous Perturbation Method for Efficient Optimization (1998)Adaptive stochastic approximation by the simultaneous perturbation method (2000)Simulation-based optimal Bayesian experimental design for nonlinear systems (2012)Prediction, Learning, and Games (2006)Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)
this work references cited by second hop

References

  • 2002Finite-time Analysis of the Multiarmed Bandit Problem5,980 cited
  • 1992Multivariate stochastic approximation using a simultaneous perturbation gradient approximation2,282 cited
  • 1998Implementation of the simultaneous perturbation algorithm for stochastic optimization797 cited
  • 2010Experimental Research in Evolutionary Computation: The New Experimentalism199 cited
  • 2005The max K-armed bandit: a new model of exploration applied to search heuristic selection114 cited
  • 2007Learning dynamic algorithm portfolios107 cited
  • 2006An asymptotically optimal algorithm for the max k-armed bandit problem77 cited

Cited by

  • 2014Bandits attack function optimization33 cited

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