Bayesian Optimisation vs. Input Uncertainty Reduction

by   Juan Ungredda, et al.

Simulators often require calibration inputs estimated from real world data and the quality of the estimate can significantly affect simulation output. Particularly when performing simulation optimisation to find an optimal solution, the uncertainty in the inputs significantly affects the quality of the found solution. One remedy is to search for the solution that has the best performance on average over the uncertain range of inputs yielding an optimal compromise solution. We consider the more general setting where a user may choose between either running simulations or instead collecting real world data. A user may choose an input and a solution and observe the simulation output, or instead query an external data source improving the input estimate enabling the search for a more focused, less compromised solution. We explicitly examine the trade-off between simulation and real data collection in order to find the optimal solution of the simulator with the true inputs. Using a value of information procedure, we propose a novel unified simulation optimisation procedure called Bayesian Information Collection and Optimisation (BICO) that, in each iteration, automatically determines which of the two actions (running simulations or data collection) is more beneficial. Numerical experiments demonstrate that the proposed algorithm is able to automatically determine an appropriate balance between optimisation and data collection.


page 1

page 2

page 3

page 4


BOP-Elites, a Bayesian Optimisation algorithm for Quality-Diversity search

Quality Diversity (QD) algorithms such as MAP-Elites are a class of opti...

Bayesian optimisation under uncertain inputs

Bayesian optimisation (BO) has been a successful approach to optimise fu...

Statistical Uncertainty Analysis for Stochastic Simulation

When we use simulation to evaluate the performance of a stochastic syste...

Evaluating Noisy Optimisation Algorithms: First Hitting Time is Problematic

A key part of any evolutionary algorithm is fitness evaluation. When fit...

Surgical task-space optimisation of the CYCLOPS robotic system

The CYCLOPS is a cable-driven parallel mechanism used for minimally inva...

Bayesian sequential design of computer experiments to estimate reliable sets

We consider an unknown multivariate function representing a system-such ...

Practical Challenges And Pitfalls Of Bluetooth Mesh Data Collection Experiments With Esp-32 Microcontrollers

Testing network algorithms in physical environments using real hardware ...

Please sign up or login with your details

Forgot password? Click here to reset