Genetic algorithms in optimizing simulated systems
WSC '95 Proceedings of the 27th conference on Winter simulation
An application of optimization-by-simulation to discrete variable systems
WSC '85 Proceedings of the 17th conference on Winter simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Simulation optimization: a survey of simulation optimization techniques and procedures
Proceedings of the 32nd conference on Winter simulation
Dynamic Control of Genetic Algorithms in a Noisy Environment
Proceedings of the 5th International Conference on Genetic Algorithms
Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics)
Black box scatter search for general classes of binary optimization problems
Computers and Operations Research
| Hi-index | 0.00 |
In many practical applications of simulation it is desirable to optimize the levels of integer or binary variables that are inputs for the simulation model. In these cases, the objective function must often be estimated through an expensive simulation process, and the optimization problem is NP-hard, leading to a computationally difficult problem. We investigate efficient solution methods for this problem, and we propose an approach that reduces the number of runs of the simulation by using ridge regression to approximate some of the simulation calls. This approach is shown to significantly decrease the computational cost but at a cost of slightly worse solution values.