One PLOT to Show Them All: Visualization of Efficient Sets in Multi-Objective Landscapes

Visualization techniques for the decision space of continuous multi-objective optimization problems (MOPs) are rather scarce in research. For long, all techniques focused on global optimality and even for the few available landscape visualizations, e.g., cost landscapes, globality is the main criterion. In contrast, the recently proposed gradient field heatmaps (GFHs) emphasize the location and attraction basins of local efficient sets, but ignore the relation of sets in terms of solution quality. In this paper, we propose a new and hybrid visualization technique, which combines the advantages of both approaches in order to represent local and global optimality together within a single visualization. Therefore, we build on the GFH approach but apply a new technique for approximating the location of locally efficient points and using the divergence of the multi-objective gradient vector field as a robust second-order condition. Then, the relative dominance relationship of the determined locally efficient points is used to visualize the complete landscape of the MOP. Augmented by information on the basins of attraction, this Plot of Landscapes with Optimal Trade-offs (PLOT) becomes one of the most informative multi-objective landscape visualization techniques available.Comment: This version has been accepted for publication at the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI

Per-instance configuration of the modularized CMA-ES by means of classifier chains and exploratory landscape analysis

Algorithms and the Foundations of Software technolog

Peeking beyond peaks: challenges and research potentials of continuous multimodal multi-objective optimization

Computer Systems, Imagery and Medi

Towards automated configuration of stream clustering algorithms

Clustering is an important technique in data analysis which can reveal hidden patterns and unknown relationships in the data. A common problem in clustering is the proper choice of parameter settings. To tackle this, automated algorithm configuration is available which can automatically find the best parameter settings. In practice, however, many of our today’s data sources are data streams due to the widespread deployment of sensors, the internet-of-things or (social) media. Stream clustering aims to tackle this challenge by identifying, tracking and updating clusters over time. Unfortunately, none of the existing approaches for automated algorithm configuration are directly applicable to the streaming scenario. In this paper, we explore the possibility of automated algorithm configuration for stream clustering algorithms using an ensemble of different configurations. In first experiments, we demonstrate that our approach is able to automatically find superior configurations and refine them over time

EPIdemiology of Surgery-Associated Acute Kidney Injury (EPIS-AKI) : Study protocol for a multicentre, observational trial

More than 300 million surgical procedures are performed each year. Acute kidney injury (AKI) is a common complication after major surgery and is associated with adverse short-term and long-term outcomes. However, there is a large variation in the incidence of reported AKI rates. The establishment of an accurate epidemiology of surgery-associated AKI is important for healthcare policy, quality initiatives, clinical trials, as well as for improving guidelines. The objective of the Epidemiology of Surgery-associated Acute Kidney Injury (EPIS-AKI) trial is to prospectively evaluate the epidemiology of AKI after major surgery using the latest Kidney Disease: Improving Global Outcomes (KDIGO) consensus definition of AKI. EPIS-AKI is an international prospective, observational, multicentre cohort study including 10 000 patients undergoing major surgery who are subsequently admitted to the ICU or a similar high dependency unit. The primary endpoint is the incidence of AKI within 72 hours after surgery according to the KDIGO criteria. Secondary endpoints include use of renal replacement therapy (RRT), mortality during ICU and hospital stay, length of ICU and hospital stay and major adverse kidney events (combined endpoint consisting of persistent renal dysfunction, RRT and mortality) at day 90. Further, we will evaluate preoperative and intraoperative risk factors affecting the incidence of postoperative AKI. In an add-on analysis, we will assess urinary biomarkers for early detection of AKI. EPIS-AKI has been approved by the leading Ethics Committee of the Medical Council North Rhine-Westphalia, of the Westphalian Wilhelms-University Münster and the corresponding Ethics Committee at each participating site. Results will be disseminated widely and published in peer-reviewed journals, presented at conferences and used to design further AKI-related trials. Trial registration number NCT04165369

The Vegan Society and social movement professionalization, 1944–2017

In a qualitative content analysis of The Vegan Society’s quarterly publication, The Vegan, spanning 73 years and nearly 300 issues, the trajectory of one of the world’s most radical and compassionate counter cuisine collectives is presented and critically assessed. The Vegan Society’s history provides a case study on the ways in which social movements negotiate difference and conflict. Specifically, this article highlights the challenges of identity, professionalization, and factionalism across the 20th and 21st centuries. This research also puts into perspective the cultural impact that veganism has had on Western society, namely the dramatic increase in vegan consumers, vegan products, and the normalcy of vegan nutrition

An Evaluation of the COVID-19 Pandemic and Perceived Social Distancing Policies in Relation to Planning, Selecting, and Preparing Healthy Meals: An Observational Study in 38 Countries Worldwide

Objectives: To examine changes in planning, selecting, and preparing healthy foods in relation to personal factors (time, money, stress) and social distancing policies during the COVID-19 crisis. Methods: Using cross-sectional online surveys collected in 38 countries worldwide in April-June 2020 (N = 37,207, Mage 36.7 SD 14.8, 77% women), we compared changes in food literacy behaviors to changes in personal factors and social distancing policies, using hierarchical multiple regression analyses controlling for sociodemographic variables. Results: Increases in planning (4.7 SD 1.3, 4.9 SD 1.3), selecting (3.6 SD 1.7, 3.7 SD 1.7), and preparing (4.6 SD 1.2, 4.7 SD 1.3) healthy foods were found for women and men, and positively related to perceived time availability and stay-at-home policies. Psychological distress was a barrier for women, and an enabler for men. Financial stress was a barrier and enabler depending on various sociodemographic variables (all p < 0.01). Conclusion: Stay-at-home policies and feelings of having more time during COVID-19 seem to have improved food literacy. Stress and other social distancing policies relate to food literacy in more complex ways, highlighting the necessity of a health equity lens. Copyright 2021 De Backer, Teunissen, Cuykx, Decorte, Pabian, Gerritsen, Matthys, Al Sabbah, Van Royen and the Corona Cooking Survey Study Group.This research was funded by the Research Foundation Flanders (G047518N) and Flanders Innovation and Entrepreneurship (HBC.2018.0397). These funding sources had no role in the design of the study, the analysis and interpretation of the data or the writing of, nor the decision to publish the manuscript.Scopu

Automated and Feature-Based Problem Characterization and Algorithm Selection Through Machine Learning

Heutzutage sind zahlreiche Abläufe strukturiert, wodurch sich diese zunächst modellieren und anschliessend sogar optimieren lassen. Selbst Probleme, die nicht durch ein mathematisches Modell repräsentiert werden können (sogenannte "Black-Box Probleme") können optimiert werden. Leider treffen Menschen hierbei tendenziell schlechte Entscheidungen, da diese oftmals auf Versuchs-und-Irrtums-Experimenten oder schlichtweg auf dem "Bauchgefuehl" der Entscheider beruhen. Sinnvoller wäre es jedoch stattdessen Optimierungsalgorithmen zu verwenden. Allerdings gibt es hiervon sehr viele, sodass sich die Frage stellt, welcher Algorithmus am besten für die Optimierung des vorliegenden Problems geeignet ist. Im Rahmen dieser kumulativen Dissertation werden einerseits automatisch berechenbare Kennzahlen zur Charakterisierung der globalen Struktur kontinuierlicher Optimierungsprobleme, und andererseits experimentelle Studien, die die Vorzüge automatisierter, sowie feature-basierter Algorithmenselektion aufzeigen, vorgestellt.Nowadays, numerous real-world workflows become more and more formalized and structured. One of the advantages of such formal processes is their accessibility for optimization. Even problems without an exact mathematical representation, i.e., so-called black-box problems, can be optimized. Unfortunately, people tend to make rather poor decisions when optimizing problems: most of the decisions are either based on numerous trial-and-error experiments or on "gut-decisions". Instead of these manual approaches, one could make use of computational power and execute an optimization algorithm. However, the plethora of optimizers leaves the user with the task of making a sophisticated guess on which of the available algorithms is best for the application at hand. Within this cumulative dissertation, a set of automatically computable features, which extracts information on the global structure of continuous optimization problems, as well as experimental studies, showing the benefits of automated and feature-based algorithm selection, are presented

Anytime behavior of inexact TSP solvers and perspectives for automated algorithm selection

Part of: IEEE WCCI 2020 is the world’s largest technical event on computational intelligence, featuring the three flagship conferences of the IEEE Computational Intelligence Society (CIS) under one roof: The 2020 International Joint Conference on Neural Networks (IJCNN 2020); the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2020); and the 2020 IEEE Congress on Evolutionary Computation (IEEE CEC 2020).The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems. The two sophisticated heuristic solvers LKH and EAX and respective (restart) variants manage to calculate closeto optimal or even optimal solutions, also for large instances with several thousand nodes in reasonable time. In this work we extend existing benchmarking studies by addressing anytime behaviour of inexact TSP solvers based on empirical runtime distributions leading to an increased understanding of solver behaviour and the respective relation to problem hardness. It turns out that performance ranking of solvers is highly dependent on the focused approximation quality. Insights on intersection points of performances offer huge potential for the construction of hybridized solvers depending on instance features. Moreover, instance features tailored to anytime performance and corresponding performance indicators will highly improve automated algorithm selection models by including comprehensive information on solver quality.Jakob Bossek, Pascal Kerschke, Heike Trautman