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Be­ne­fits and Per­spec­ti­ves of Au­to­ma­ted Al­go­rithm Se­lec­ti­on for the Tra­ve­ling Sa­les­per­son Pro­blem

Ort: Zoom (see details)
Veranstalter: DaSCo


The Euclidean Traveling Salesperson Problem (TSP) is a classical optimization problem which is of high relevance for science and industry. Although it has been well-studied for decades, there is no algorithm in the class of inexact TSP optimization that is superior to all its competitors. Consequently, choosing the "right" algorithm for optimizing a given TSP instance is a challenging task in itself, and choosing the "wrong" algorithm can have severe implications on the overall performance. In recent years, automated algorithm selection has proven to be a very effective method to address this challenge in an automated way, thus improving the state of the art in this particular optimization domain. In my presentation, I will summarize the status quo of automated algorithm selection for the TSP and present some research perspectives in times of automation, digitization and big data.


Pascal Kerschke, Data Science: Statistics and Optimization, University of Münster


The lecture will be virtual and take place in Zoom:

Meeting ID: 972 1770 9819
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Meeting ID: 972 1770 9819
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