Jakob Bossek
University of Münster
85 Papers
128 Citations
Jakob Bossek is an academic researcher from University of Münster. The author has contributed to research in topics: Evolutionary algorithm & Computer science. The author has an hindex of 12, co-authored 76 publications. Previous affiliations of Jakob Bossek include University of Adelaide & Technical University of Dortmund.
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Papers
•Posted Content
Benchmarking in Optimization: Best Practice and Open Issues
Thomas Bartz-Beielstein,Carola Doerr,Jakob Bossek,Sowmya Chandrasekaran,Tome Eftimov,Andreas Fischbach,Pascal Kerschke,Manuel López-Ibáñez,Katherine M. Malan,Jason H. Moore,Boris Naujoks,Patryk Orzechowski,Vanessa Volz,Markus Wagner,Thomas Weise +14 more
TL;DR: The article discusses eight essential topics in benchmarking: clearly stated goals, well-specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient designs, comprehensible presentations, and guaranteed reproducibility.
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Leveraging TSP Solver Complementarity through Machine Learning
TL;DR: This work directly compares five state-of-the-art inexact solvers—namely, LKH, EAX, restart variants of those, and MAOS—on a large set of well-known benchmark instances and demonstrates complementary performance, in that different instances may be solved most effectively by different algorithms.
Single and Multi-Objective Optimization Test Functions
TL;DR: The smoof package implements a large set of test functions and test function generators for both the single and multi-objective case in continuous optimization and provides functions to easily create own test functions.
Evolving diverse TSP instances by means of novel and creative mutation operators
Jakob Bossek,Pascal Kerschke,Aneta Neumann,Markus Wagner,Frank Neumann,Heike Trautmann +5 more
- 27 Aug 2019
TL;DR: New and creative mutation operators for evolving instances of the Traveling Salesperson Problem are introduced and it is shown that adopting those operators in an evolutionary algorithm allows for the generation of benchmark sets with highly desirable properties.
71
Local search and the traveling salesman problem: A feature-based characterization of problem hardness
Olaf Mersmann,Bernd Bischl,Jakob Bossek,Heike Trautmann,Markus Wagner,Frank Neumann +5 more
- 01 Jan 2012
TL;DR: This paper takes a statistical approach and examines the features of TSP instances that make the problem either hard or easy to solve, using the approximation ratio that it achieves on a given instance as a measure of problem difficulty.
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