Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Parallel Recombinative Reinforcement Learning (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Dynamic Control of Genetic Algorithms in a Noisy Environment
Proceedings of the 5th International Conference on Genetic Algorithms
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Some topics for simulation optimization
Proceedings of the 40th Conference on Winter Simulation
Reinforcement Learning and Reactive Search: an adaptive MAX-SAT solver
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Particle Swarm Optimization and Intelligence: Advances and Applications
Particle Swarm Optimization and Intelligence: Advances and Applications
Stochastic Simulation Optimization: An Optimal Computing Budget Allocation
Stochastic Simulation Optimization: An Optimal Computing Budget Allocation
Integrating techniques from statistical ranking into evolutionary algorithms
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
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Noisy optimization problems arise very often in real-life applications. A common practice to tackle problems characterized by uncertainties, is the re-evaluation of the objective function at every point of interest for a fixed number of replications. The obtained objective values are then averaged and their mean is considered as the approximation of the actual objective value. However, this approach can prove inefficient, allocating replications to unpromising candidate solutions. We propose a hybrid approach that integrates the established Particle Swarm Optimization algorithm with the Reinforcement Learning approach to efficiently tackle noisy problems by intelligently allocating the available computational budget. Two variants of the proposed approach, based on different selection schemes, are assessed and compared against the typical alternative of equal sampling. The results are reported and analyzed, offering significant evidence regarding the potential of the proposed approach.