COSEAL - Configuration and Selection of Algorithms

The COSEAL research group is an international consortium of researchers from all over the world (e.g. Belgium, Canada, Ireland, Denmark and Germany) which addresses current challenges from Algorithm Selection, Algorithm Configuration and Machine Learning.

DemoRESILdigital: Democratic resilience in times of online-propaganda, fake news, fear- and hate speech.

Joint research project: Identifying, verifying, and stopping concealed propaganda attacks via online media - Sub-Project: Coordination of simulation, identification, and defense against concealed propaganda attacks

This project addresses the identification and verification of (semi-)automated concealed propaganda in online media (e.g. in social networks) as an interdisciplinary approach.

ERCIS Omni-Channel Lab - powered by Arvato

The ERCIS Omni-Channel Lab, is a research project in collaboration with Arvato CRM Solutions. The Lab combines the academic research and teaching activities of ERCIS and Arvato’s practical experience of delivering tech-enabled Omni-Channel CRM solutions from 110 global locations for many of the world’s best-known brands. Its areas of investigation focus around ‘Processes’, ‘Data’ and ‘Analytics’.

Instance-Based Algorithm Selection of Inexact TSP solvers

The Travelling Salesperson Problem (TSP) is arguably the most prominent NP-hard combinatorial optimisation problem. Given a set of n cities and pairwise distances between those, the objective in the TSP is to find the shortest round-trip or tour through all cities, i.e., a sequence in which every city is visited exactly once, the start and end cities are identical, and the total length of the tour is minimal. The Euclidean TSP has important applications, e.g., in the fabrication of printed circuit boards as well as in transportation and logistics. We aim at constructing an instance-based algorithm selection model in order to improve the current state-of-the-art solver.

Architectures for Evolutionary Multi-Objective Algorithms to support User Expertise Integration

This project strives to analyze existing algorithm architectures and design new structures such that integration of expertise becomes seamlessly possible. Users should be free of the need to redesign or deeply understand the general algorithms structure.

9th International Conference on Evolutionary Multi- Criterion Optimization, Münster 19. - 22.03.2017

EMO 2017 is the 9th International Conference on Evolutionary Multi- Criterion Optimization, aiming to continue the success of previous EMO conferences. We will bring together both the EMO and the multiple criteria decision making (MCDM) communities and moreover focus on solving real-world problems in government, business and industry. The classical EMO format will be supplemented by an EMO competition.

Hybridization of indicator-based metaheuristics with modern local search methods in multiobjective optimization

The project realizes international expertise exchange between German and Mexican researchers in the context of hybrid evolutionary multi-objective optimization. A special focus lies on integrating local search into state-of-the-art meta-heuristics like SMS-EMOA and dP-EMOA.

Google Summer of Code 2015: Improving mlr's hyperparameter and tuning system for efficient model selection

This "Google Summer of Code 2015" project aims at enriching mlr's hyperparamter system with a-priori knowledge. The goal of the project is to make the hyperparameter configuration and tuning more flexible, efficient and convenient.

Addressing Current Challenges in Evolutionary Multi-Objective Optimization: Indicator-based Selection, Convergence and Applicability

This project aims to initiate and intensify bi-lateral collaboration of researchers from Brazil and Germany under the umbrella of current research topics in the domain of evolutionary multi-objective optimization.

Structural optimization of distributed energy supply systems - applied to chemical parks

The project aimed at setting up a method for structural optimization that enables the planning engineer to design a distributed energy supply system of near-optimal quality.