Efficient Computation of Expected Hypervolume Improvement Using Box Decomposition Algorithms

In the field of multi-objective optimization algorithms, multi-objective Bayesian Global Optimization (MOBGO) is an important branch, in addition to evolutionary multi-objective optimization algorithms (EMOAs). MOBGO utilizes Gaussian Process models learned from previous objective function evaluations to decide the next evaluation site by maximizing or minimizing an infill criterion. A common criterion in MOBGO is the Expected Hypervolume Improvement (EHVI), which shows a good performance on a wide range of problems, with respect to exploration and exploitation. However, so far it has been a challenge to calculate exact EHVI values efficiently. In this paper, an efficient algorithm for the computation of the exact EHVI for a generic case is proposed. This efficient algorithm is based on partitioning the integration volume into a set of axis-parallel slices. Theoretically, the upper bound time complexities are improved from previously $O (n^2)$ and $O(n^3)$, for two- and three-objective problems respectively, to $\Theta(n\log n)$, which is asymptotically optimal. This article generalizes the scheme in higher dimensional case by utilizing a new hyperbox decomposition technique, which was proposed by D{\"a}chert et al, EJOR, 2017. It also utilizes a generalization of the multilayered integration scheme that scales linearly in the number of hyperboxes of the decomposition. The speed comparison shows that the proposed algorithm in this paper significantly reduces computation time. Finally, this decomposition technique is applied in the calculation of the Probability of Improvement (PoI)

Parallelisation of genetic algorithms for the 2-page crossing number problem

Genetic algorithms have been applied to solve the 2-page crossing number problem successfully, but since they work with one global population, the search time and space are limited. Parallelisation provides an attractive prospect to improve the efficiency and solution quality of genetic algorithms. This paper investigates the complexity of parallel genetic algorithms (PGAs) based on two evaluation measures: Computation-time to Communication-time and Population-size to Chromosomesize. Moreover, the paper unifies the framework of PGA models with the function PGA (subpopulation size; cluster size, migration period; topology), and explores the performance of PGAs for the 2-page crossing number problem

Parallel Processes in HPX: Designing an Infrastructure for Adaptive Resource Management

Advancement in cutting edge technologies have enabled better energy efficiency as well as scaling computational power for the latest High Performance Computing(HPC) systems. However, complexity, due to hybrid architectures as well as emerging classes of applications, have shown poor computational scalability using conventional execution models. Thus alternative means of computation, that addresses the bottlenecks in computation, is warranted. More precisely, dynamic adaptive resource management feature, both from systems as well as application\u27s perspective, is essential for better computational scalability and efficiency. This research presents and expands the notion of Parallel Processes as a placeholder for procedure definitions, targeted at one or more synchronous domains, meta data for computation and resource management as well as infrastructure for dynamic policy deployment. In addition to this, the research presents additional guidelines for a framework for resource management in HPX runtime system. Further, this research also lists design principles for scalability of Active Global Address Space (AGAS), a necessary feature for Parallel Processes. Also, to verify the usefulness of Parallel Processes, a preliminary performance evaluation of different task scheduling policies is carried out using two different applications. The applications used are: Unbalanced Tree Search, a reference dynamic graph application, implemented by this research in HPX and MiniGhost, a reference stencil based application using bulk synchronous parallel model. The results show that different scheduling policies provide better performance for different classes of applications; and for the same application class, in certain instances, one policy fared better than the others, while vice versa in other instances, hence supporting the hypothesis of the need of dynamic adaptive resource management infrastructure, for deploying different policies and task granularities, for scalable distributed computing

Comparison of image processing techniques for nonviable tissue quantification in late gadolinium enhancement cardiac magnetic resonance images

Purpose: The aim of this study was to compare the performance of quantitative methods, either semiautomated or automated, for left ventricular (LV) nonviable tissue analysis from cardiac magnetic resonance late gadolinium enhancement (CMR-LGE) images. Materials and Methods: The investigated segmentation techniques were: (i) n-standard deviations thresholding; (ii) full width at half maximum thresholding; (iii) Gaussian mixture model classification; and (iv) fuzzy c-means clustering. These algorithms were applied either in each short axis slice (single-slice approach) or globally considering the entire short-axis stack covering the LV (global approach). CMR-LGE images from 20 patients with ischemic cardiomyopathy were retrospectively selected, and results from each technique were assessed against manual tracing. Results: All methods provided comparable performance in terms of accuracy in scar detection, computation of local transmurality, and high correlation in scar mass compared with the manual technique. In general, no significant difference between single-slice and global approach was noted. The reproducibility of manual and investigated techniques was confirmed in all cases with slightly lower results for the nSD approach. Conclusions: Automated techniques resulted in accurate and reproducible evaluation of LV scars from CMR-LGE in ischemic patients with performance similar to the manual technique. Their application could minimize user interaction and computational time, even when compared with semiautomated approaches