Evolving diverse TSP instances by means of novel and creative mutation operators

Author:

Bossek Jakob1,Kerschke Pascal1,Neumann Aneta2,Wagner Markus2,Neumann Frank2,Trautmann Heike1

Affiliation:

1. University of Münster, Germany

2. The University of Adelaide, Australia

Publisher

ACM Press

Reference34 articles.

1. Bradley Alexander, James Kortman, and Aneta Neumann. 2017. Evolution of artistic image variants through feature based diversity optimisation. InProceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017, Berlin, Germany, July 15--19, 2017, Peter A. N. Bosman (Ed.). ACM, 171--178.

2. David L. Applegate, Robert E. Bixby, Vasek Chvátal, and William J. Cook. 2007.The Traveling Salesman Problem: A Computational Study. Princeton University Press, Princeton, NJ, USA.

3. Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Thomas Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, and Joaquin Vanschoren. 2016. ASlib: A Benchmark Library for Algorithm Selection.Artificial Intelligence (AIJ)237 (2016), 41 -- 58.

4. Jakob Bossek and Heike Trautmann. 2016. Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers. InProceedings of the 10th International Conference on Learning and Intelligent Optimization (LION). Springer, 48 -- 59.

5. Jakob Bossek and Heike Trautmann. 2016. Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference. InAdvances in Artificial Intelligence (AI*IA). Springer, 3--12.

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