Skip to Main content Skip to Navigation
Journal articles

Evolution Control for parallel ANN-assisted simulation-based optimization application to Tuberculosis Transmission Control

Abstract : In many optimal design searches, the function to optimise is a simulator that is computationally expensive. While current High Performance Computing (HPC) methods are not able to solve such problems efficiently, parallelism can be coupled with approximate models (surrogates or meta-models) that imitate the simulator in timely fashion to achieve better results. This combined approach reduces the number of simulations thanks to surrogate use whereas the remaining evaluations are handled by supercomputers. While the surrogates' ability to limit computational times is very attractive, integrating them into the over-arching optimization process can be challenging. Indeed, it is critical to address the major trade-off between the quality (precision) and the efficiency (execution time) of the resolution. In this article, we investigate Evolution Controls (ECs) which are strategies that define the alternation between the simulator and the surrogate within the optimization process. We propose a new EC based on the prediction uncertainty obtained from Monte Carlo Dropout (MCDropout), a technique originally dedicated to quantifying uncertainty in deep learning. Investigations of such uncertainty-aware ECs remain uncommon in surrogate-assisted evolutionary optimization. In addition, we use parallel computing in a complementary way to address the high computational burden. Our new strategy is implemented in the context of a pioneering application to Tuberculosis Transmission Control. The reported results show that the MCDropout-based EC coupled with massively parallel computing outperforms strategies previously proposed in the field of surrogate-assisted optimization.
Complete list of metadata

Cited literature [59 references]  Display  Hide  Download

https://hal.inria.fr/hal-02904840
Contributor : Guillaume Briffoteaux <>
Submitted on : Wednesday, July 22, 2020 - 5:08:10 PM
Last modification on : Tuesday, December 15, 2020 - 9:03:51 AM
Long-term archiving on: : Tuesday, December 1, 2020 - 5:13:46 AM

File

article_FGCS_2019.pdf
Files produced by the author(s)

Identifiers

Citation

Guillaume Briffoteaux, Romain Ragonnet, Mohand Mezmaz, Nouredine Melab, Daniel Tuyttens. Evolution Control for parallel ANN-assisted simulation-based optimization application to Tuberculosis Transmission Control. Future Generation Computer Systems, Elsevier, 2020, 113, pp.454-467. ⟨10.1016/j.future.2020.07.005⟩. ⟨hal-02904840⟩

Share

Metrics

Record views

142

Files downloads

293