ICCS 2019 Main Track (MT) Session 11

Time and Date: 16:50 - 18:30 on 12th June 2019

Room: 1.3

Chair: Ricardo Simões

331 Robust Ensemble-Based Evolutionary Calibration of the Numerical Wind Wave Model [abstract]
Abstract: The adaptation of numerical wind wave models to the local time-spatial conditions is a problem that can be solved by using various calibration techniques. However, the obtained sets of physical parameters become over-tuned to specific events if there is a lack of observations. In this paper, we propose a robust evolutionary calibration approach that allows to build the stochastic ensemble of perturbed models and use it to achieve the trade-off between quality and robustness of the target model. The implemented robust ensemble-based evolutionary calibration (REBEC) approach was compared to the baseline SPEA2 algorithm in a set of experiments with the SWAN wind wave model configuration for the Kara Sea domain. Provided metrics for the set of scenarios confirm the effectiveness of the REBEC approach for the majority of calibration scenarios.
Pavel Vychuzhanin, Nikolay Nikitin and Anna Kalyuzhnaya
438 Approximate Repeated Administration Models for Pharmacometrics [abstract]
Abstract: Employing multiple processes in parallel is a common approach to reduce running-times in high-performance computing applications. However, improving performance through parallelization is only part of the story. At some point, all available parallelism is exploited and performance improvements need to be sought elsewhere. As part of drug development trials, a compound is periodically administered, and the interactions between it and the human body are modeled through pharmacokinetics and pharmacodynamics by a set of ordinary differential equations. Numeric integration of these equations is the most computationally intensive part of the fitting process. For this task, parallelism brings little benefit. This paper describes how to exploit the nearly periodic nature of repeated administration models by numeric application of the method of averaging on the one hand and reusing previous computational effort on the other hand. The presented method can be applied on top of any existing integrator while requiring only a single tunable threshold parameter. Performance improvements and approximation error are studied on two pharmacometrics models. In addition, automated tuning of the threshold parameter is demonstrated in two scenarios. Up to 1.7-fold and 70-fold improvements are measured with the presented method for the two models respectively.
Balazs Nemeth, Tom Haber, Jori Liesenborgs and Wim Lamotte
466 Evolutionary Optimization of Intruder Interception Plans for Mobile Robot Groups [abstract]
Abstract: The task of automated intruder detection and interception is often considered as a suitable application for groups of mobile robots. Realistic versions of the problem include representing uncertainty, which turns it into NP-hard optimization tasks. In this paper we define the problem of indoor intruder interception with probabilistic intruder motion model and uncertainty of intruder detection. We define a model for representing the problem and propose an algorithm for optimizing plans for groups of mobile robots patrolling the building. The proposed evolutionary multi-agent algorithm uses a novel representation of solutions. The algorithm has been evaluated using different problem sizes and compared with other methods.
Wojciech Turek, Agata Kubiczek and Aleksander Byrski
434 Synthesizing quantum circuits via numerical optimization [abstract]
Abstract: We provide a simple framework for the synthesis of quantum circuits based on a numerical optimization algorithm. This algorithm is used in the context of the trapped-ions technology. We derive theoretical lower bounds for the number of quantum gates required to implement any quantum algorithm. Then we present numerical experiments with random quantum operators where we compute the optimal parameters of the circuits and we illustrate the correctness of the theoretical lower bounds. We finally discuss the scalability of the method with the number of qubits.
Timothée Goubault de Brugière, Marc Baboulin, Benoît Valiron and Cyril Allouche
455 Application of continuous time quantum walks to image segmentation [abstract]
Abstract: This paper provides the new algorithm that applies concept of continuous time quantum walks to image segmentation problem. The work, inspired by results from its classical counterpart, presents and compares two versions of the solution regarding calculation of pixel-segment association: the version using limiting distribution of the walk and the version using last step distribution. The obtained results vary in terms of accuracy and possibilities to be ported to a real quantum device. The described results were obtained by simulation on classical computer, but the algorithms were designed in a way that will allow to use a real quantum computer, when ready.
Michał Krok, Katarzyna Rycerz and Marian Bubak