Algorithmization and Social Interaction

Imagine you call a company and your request is no longer answered by a human being but by an artificial assistant - how does this affect you as an individual and society at large? And does the customization of information in social media and online environments limit our horizon, or even keeps us in a ‚filter bubble‘? These are just a few of the socially and politically relevant core questions of the interdisciplinary topical program „Algorithmization and Social Interaction“. Scholars from information systems, economy, social sciences, law and communication studies work together to explore, first, how (artificially intelligent) algorithms can be used to influence social interaction. Second, the topical program is interested in how society (including the public as well as political and societal elites) reacts to this increasing algorithmic governance.

Using Evolutionary Algorithms for Diversity Optimization

This project is one of the STSMs (Short Term Scientific Missions) of the COST (European Cooperation in Science and Technology) Action CA15140 on "Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO)", which aims at bridging the gap between theory and practice of nature inspired optimization algorithms.

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.

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.

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.

Benchmarking Network

The Benchmarking Network is an initiative that has emerged in summer 2019, with the idea to consolidate and to stimulate activities on benchmarking iterative optimization heuristics such as local search algorithms, swarm intelligence techniques, model- and/or surrogate-based heuristics, etc - in short, all algorithms that work by a sequential evaluation of solution candidates.