Optimization Knowledge Center: A Decision Support System for Heuristic Optimization

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

2 Zitate (Scopus)


The task of selecting an appropriate algorithm instance for a given optimization problem instance often requires significant experience. Efficient optimization requires a different set of parameters or an entirely different algorithmic approach for some characteristics of problem instances. Obtaining such experience takes significant amount of time and requires an in-depth analysis of the algorithms' performance. In addition to these difficulties, published results only provide a summary, the obtained raw performance data is often not reused later on. In this work we want to give such data more value and more publicity by storing it in a database and reusing it when solving new problem instances. We describe the information that the data should contain in order to maximize reusability. Furthermore, we discuss three use cases that supports optimization experts in their decisions and allows them to perform a manual exploration of the search space using available algorithm instances and the possibility to decide on the starting solutions and thus bias the search in a certain sub-space of the solution space.
TitelGECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
Redakteure/-innenTobias Friedrich
Herausgeber (Verlag)ACM Sigevo
ISBN (elektronisch)9781450343237
ISBN (Print)978-1-4503-4323-7
PublikationsstatusVeröffentlicht - 20 Juli 2016
VeranstaltungGenetic and Evolutionary Computation Conference (GECCO 2016) - Denver, Colorado, USA/Vereinigte Staaten
Dauer: 20 Juli 201624 Juli 2016


NameGECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference


KonferenzGenetic and Evolutionary Computation Conference (GECCO 2016)
Land/GebietUSA/Vereinigte Staaten
OrtDenver, Colorado


  • decision-support-system
  • knowledge base
  • heuristic optimization