on Foundations of Genetic Algorithms, pages 58–71.
ACM.
Bossek, J. and Neumann, F. (2022). Exploring the fea-
ture space of TSP instances using quality diversity. In
Genetic and Evolutionary Computation Conf., pages
186–194. ACM.
Bossek, J. and Trautmann, H. (2016). Understand-
ing characteristics of evolved instances for state-of-
the-art inexact TSP solvers with maximum perfor-
mance difference. In Advances in Artificial Intelli-
gence (AI*IA’16), pages 3–12. Springer.
ˇ
Cern
´
y, V. (1985). Thermodynamical approach to the trav-
eling salesman problem: An efficient simulation algo-
rithm. Journal of Optimization Theory and Applica-
tions, 45(1):41–51.
Chiong, R., Weise, T., and Michalewicz, Z. (2012). Vari-
ants of Evolutionary Algorithms for Real-World Ap-
plications. Springer.
de Bruin, E., Thomson, S. L., and van den Berg, D. (2023).
Frequency fitness assignment on JSSP: A critical re-
view. In European Conf. on Applications of Evolu-
tionary Computation, pages 351–363.
Floyd, R. W. (1962). Algorithm 97: Shortest path. Commu-
nications of the ACM, 5(6):345.
Gutin, G. Z. and Punnen, A. P., editors (2002). The Travel-
ing Salesman Problem and its Variations. Springer.
Hansen, N., Auger, A., Ros, R., Mersmann, O., Tu
ˇ
sar, T.,
and Brockhoff, D. (2021). COCO: a platform for com-
paring continuous optimizers in a black-box setting.
Optimization Methods and Software, 36(1):114–144.
Helsgaun, K. (2009). General k-opt submoves for the Lin–
Kernighan TSP heuristic. Mathematical Program-
ming Computation, 1(2-3):119–163.
Hoos, H. H. and St
¨
utzle, T. (2005). Stochastic Local Search:
Foundations and Applications. Elsevier.
Johnson, D. S. and McGeoch, L. A. (2008). 8th DIMACS
Implementation Challenge: The Traveling Salesman
Problem. Rutgers University.
Kirkpatrick, S., Gelatt, Jr., C. D., and Vecchi, M. P. (1983).
Optimization by simulated annealing. Science Maga-
zine, 220(4598):671–680.
Lawler, E. L., Lenstra, J. K., Rinnooy Kan, A. H. G., and
Shmoys, D. B. (1985). The Traveling Salesman Prob-
lem: A Guided Tour of Combinatorial Optimization.
Wiley Interscience.
Liang, T., Wu, Z., L
¨
assig, J., van den Berg, D., Thom-
son, S. L., and Weise, T. (2024). Addressing the
traveling salesperson problem with frequency fitness
assignment and hybrid algorithms. Soft Computing.
doi:10.1007/s00500-024-09718-8.
Liang, T., Wu, Z., L
¨
assig, J., van den Berg, D., and Weise, T.
(2022). Solving the traveling salesperson problem us-
ing frequency fitness assignment. In IEEE Symposium
Series on Computational Intelligence, pages 360–367.
Mersmann, O., Bischl, B., Bossek, J., and et al. (2012).
Local search and the traveling salesman problem: A
feature-based characterization of problem hardness.
In Intl. Conf. on Learning and Intelligent Optimiza-
tion, pages 115–129. Springer.
Nagata, Y. and Soler, D. (2012). A new genetic algorithm
for the asymmetric traveling salesman problem. Ex-
pert Systems with Applications, 39(10):8947–8953.
Nallaperuma, S., Wagner, M., Neumann, F., Bischl, B.,
Mersmann, O., and Trautmann, H. (2012). Features of
easy and hard instances for approximation algorithms
and the traveling salesperson problem. In Works. on
Automated Selection and Tuning of Algorithms, Intl.
Conf. Parallel Problem Solving from Nature.
Neumann, A., Gao, W., Doerr, C., Neumann, F., and Wag-
ner, M. (2018). Discrepancy-based evolutionary diver-
sity optimization. In Genetic and Evolutionary Com-
putation Conf., pages 991–998. ACM.
Neumann, A., Gao, W., Wagner, M., and Neumann, F.
(2019). Evolutionary diversity optimization using
multi-objective indicators. In Genetic and Evolution-
ary Computation Conf., pages 837–845. ACM.
Reinelt, G. (1991). TSPLIB – a traveling salesman problem
library. ORSA Journal on Computing, 3(4):376–384.
Warshall, S. (1962). A theorem on boolean matrices. Jour-
nal of the ACM, 9(1):11–12.
Weise, T. (2009). Global Optimization Algorithms – The-
ory and Application. Institute of Applied Optimiza-
tion, Hefei University. http://iao.hfuu.edu.cn/images/
publications/W2009GOEB.pdf.
Weise, T., Chiong, R., Tang, K., L
¨
assig, J., Tsutsui, S.,
Chen, W., Michalewicz, Z., and Yao, X. (2014a).
Benchmarking optimization algorithms: An open
source framework for the traveling salesman problem.
IEEE Computational Intelligence Magazine, 9(3):40–
52.
Weise, T., Li, X., Chen, Y., and Wu, Z. (2021a). Solving
job shop scheduling problems without using a bias for
good solutions. In Genetic and Evolutionary Compu-
tation Conf. Companion, pages 1459–1466. ACM.
Weise, T., Wan, M., Tang, K., Wang, P., Devert, A.,
and Yao, X. (2014b). Frequency fitness assign-
ment. IEEE Transactions on Evolutionary Computa-
tion, 18(2):226–243.
Weise, T., Wu, Y., Chiong, R., Tang, K., and L
¨
assig,
J. (2016). Global versus local search: The impact
of population sizes on evolutionary algorithm perfor-
mance. Journal of Global Optimization, 66:511–534.
Weise, T., Wu, Z., Li, X., and Chen, Y. (2021b). Frequency
fitness assignment: Making optimization algorithms
invariant under bijective transformations of the objec-
tive function value. IEEE Transactions on Evolution-
ary Computation, 25(2):307–319.
Weise, T., Wu, Z., Li, X., Chen, Y., and L
¨
assig, J. (2023).
Frequency fitness assignment: Optimization without
bias for good solutions can be efficient. IEEE Transac-
tions on Evolutionary Computation, 27(4):980–992.
Whitley, L. D., Hains, D., and Howe, A. (2010). A hybrid
genetic algorithm for the traveling salesman problem
using generalized partition crossover. In Intl. Conf.
on Parallel Problem Solving from Nature, pages 566–
575. Springer.
ECTA 2024 - 16th International Conference on Evolutionary Computation Theory and Applications
180