• definitely finished

    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.

    Project status definitely finished
    Project time 01.01.2017- 31.12.2018
    Funding source German Academic Exchange Service
    Project number 57314626
    Keywords Algorithm Selection; TSP; automated algorithm selection; inexact solvers; Information Systems; Statistics; Canada


    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.

    Project status definitely finished
    Project time 19.03.2017- 22.03.2017
    Website http://www.emo2017.org
    Funding source Participation / conference fees
    Project number TR 891/9-1
    Keywords Evolutionary Multiobjective Optimization


  • in progress

    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.

    Project status in progress
    Project time 01.02.2013- 01.01.2030
    Website http://www.coseal.net
    Keywords algorithm selection; configuration; 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.

    Project status in progress
    Project time since 01.12.2019
    Website https://sites.google.com/view/benchmarking-network
    Keywords Benchmarking; Optimization; Machine Learning; Research Network