Abstract
Crossover for evolutionary algorithms applied to permutation problems is a difficult and widely discussed topic. In this paper we use ideas from ant colony optimization to design a new permutation crossover operator. One of the advantages of the new crossover operator is the ease to introduce problem specific heuristic knowledge. Empirical tests on a travelling salesperson problem show that the new crossover operator yields excellent results and significantly outperforms evolutionary algorithms with edge recombination operator as well as pure ant colony optimization.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
J. C. Bean. Genetic algorithms and random keys for sequencing and optimization. ORSA Journal on Computing, 6(2):154–160, 1994.
C. Bierwirth, D.C. Mattfeld, and H. Kopfer. On permutation representations for scheduling problems. In H.-M. Voigt, editor, Parallel Problem Solving from Nature, volume 1141 of LNCS, pages 310–318. Springer, Berlin, 1996.
E. Bonabeau, M. Dorigo, and G. Theraulaz. Swarm intelligence: from natural to artificial systems. Oxford University Press, 1999.
H. M. Botee and E. Bonabeau. Evolving ant colonies. Advanced Complex Systems, 1:149–159, 1998.
L. Davis. Applying adaptive algorithms to epistatic domains. In International Joint Conference on Artificial Intelligence, pages 162–164, 1985.
M. Dorigo and G. Di Caro. The ant colony optimization meta-heuristic. In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pages 11–32. McGraw-Hill, 1999.
B. Freisleben and P. Merz. New genetic local search operators for the traveling salesman problem. In Hans-Michael Voigt, Werner Ebeling, Ingo Rechenberg, and Hans-Paul Schwefel, editors, Parallel Problem Solving from Nature, volume 1141, pages 890–899, Berlin, 1996. Springer.
D. E. Goldberg and R. Lingle. Alleles, loci, and the TSP. In J. J. Grefenstette, editor, First International Conference on Genetic Algorithms, pages 154–159. Lawrence Erlbaum Associates, 1985.
J. J. Grefenstette. Incorporating problem specific knowledge into genetic algorithms. In Genetic Algorithms and Simulated Annealing, pages 42–60. Morgan Kaufmann, 1987.
M. Guntsch and M. Middendorf. A population based approach for ACO. In European Workshop on Evolutionary Computation in Combinatorial Optimization, volume 2279 of LNCS, pages 72–81. Springer, 2002.
B. A. Julstrom and G. R. Raidl. Weight-biased edge-crossover in evolutionary algorithms for two graph problems. In G. Lamont, J. Carroll, H. Haddad, D. Morton, G. Papadopoulos, R. Sincovec, and A. Yfantis, editors, 16th ACM Symposium on Applied Computing, pages 321–326. ACM Press, 2001.
S. Jung and B.-R. Moon. Toward minimal restriction of genetic encoding and crossovers for the two-dimensional Euclidean TSP. IEEE Transactions on Evolutionary Computation, 6(6):557–565, 2002.
V. V. Miagkikh and W. F. Punch. An approach to solving combinatorial optimization problems using a population of reinforcement learning agents. In Genetic and Evolutionary Computation Conference, pages 1358–1365, 1999.
V. V. Miagkikh and W. F. Punch. A generalized approach to handling parameter interdependencies in probabilistic modeling and reinforcement learning optimization algorithms. In Workshop on Frontiers in Evolutionary Algorithms, 2000.
Y. Nagata and S. Kobayashi. Edge assembly crossover: A high-power genetic algorithm for the traveling salesman problem. In T. Bäck, editor, International Conference on Genetic Algorithms, pages 450–457. Morgan Kaufmann, 1997.
G. Reinelt. TSPLIB-a travelling salesman problem library. ORSA Journal on Computing, 3:376–384, 1991.
A.Y.-C. Tang and K.-S. Leung. A modified edge recombination operator for the travelling salesman problem. In Parallel Problem Solving from Nature II, volume 866 of LNCS, pages 180–188, Berlin, 1994. Springer.
G. Tao and Z. Michalewicz. Evolutionary algorithms for the TSP. In A. E. Eiben, T. Bäck, M. Schoenauer, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature, volume 1498 of LNCS, pages 803–812. Springer, 1998.
http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/ index.html.
D. Whitley, T. Starkweather, and D’A. Fuquay. Scheduling problems and traveling salesman: The genetic edge recombination operator. In J. Schaffer, editor, International Conference on Genetic Algorithms, pages 133–140. Morgan Kaufmann, 1989.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Branke, J., Barz, C., Behrens, I. (2003). Ant-Based Crossover for Permutation Problems. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_90
Download citation
DOI: https://doi.org/10.1007/3-540-45105-6_90
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40602-0
Online ISBN: 978-3-540-45105-1
eBook Packages: Springer Book Archive