Designing Pheromone Update Strategies with Strongly Typed Genetic Programming

  • Jorge Tavares
  • Francisco B. Pereira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6621)


Ant Colony algorithms are population-based methods widely used in combinatorial optimization problems. We propose a strongly typed genetic programming approach to automatically evolve the communication mechanism that allows ants to cooperatively solve a given problem. Results obtained with several TSP instances show that the evolved pheromone update strategies are effective, exhibit a good generalization capability and are competitive with human designed variants.


Travel Salesperson Problem Pheromone Level Genetic Program Algorithm Pheromone Matrix Standard Genetic Program 
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  1. 1.
    Botee, H.M., Bonabeau, E.: Evolving ant colony optimization. Advanced Complex Systems 1, 149–159 (1998)CrossRefGoogle Scholar
  2. 2.
    Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Qu, R.: Hyper-heuristics: A survey of the state of the art. Tech. Rep. NOTTCS-TR-SUB-0906241418-2747, University of Nottingham (2010)Google Scholar
  3. 3.
    Diosan, L., Oltean, M.: Evolving the structure of the particle swarm optimization algorithms. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2006. LNCS, vol. 3906, pp. 25–36. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Diosan, L., Oltean, M.: Evolutionary design of evolutionary algorithms. Genetic Programming and Evolvable Machines 10(3), 263–306 (2009)CrossRefGoogle Scholar
  5. 5.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)zbMATHGoogle Scholar
  6. 6.
    Krasnogor, N., Blackburnem, B., Hirst, J., Burke, E.: Multimeme algorithms for protein structure prediction. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 769–778. Springer, Heidelberg (2002)Google Scholar
  7. 7.
    Montana, D.J.: Strongly typed genetic programming. Evolutionary Computation Journal 3(2), 199–230 (1995)CrossRefGoogle Scholar
  8. 8.
    Oltean, M.: Evolving evolutionary algorithms using linear genetic programming. Evolutionary Computation Journal 13, 387–410 (2005)CrossRefGoogle Scholar
  9. 9.
    Poli, R., Langdon, W.B., Holland, O.: Extending particle swarm optimisation via genetic programming. In: Keijzer, M., Tettamanzi, A.G.B., Collet, P., van Hemert, J., Tomassini, M. (eds.) EuroGP 2005. LNCS, vol. 3447, pp. 291–300. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Poli, R., Langdon, W.B., McPhee, N.F.: A field guide to genetic programming (2008); Published via and freely available at (With contributions by J. R. Koza)
  11. 11.
    Runka, A.: Evolving an edge selection formula for ant colony optimization. In: GECCO 2009 Proceedings, pp. 1075–1082 (2009)Google Scholar
  12. 12.
    Stutzle, T., Hoos, H.: Max-min ant system and local search for the traveling salesman problem. In: ICEC Proceedings, pp. 309–314. IEEE Press, Los Alamitos (1997)Google Scholar
  13. 13.
    Tavares, J., Pereira, F.B.: Evolving strategies for updating pheromone trails: A case study with the tsp. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6239, pp. 523–532. Springer, Heidelberg (2010)Google Scholar
  14. 14.
    White, T., Pagurek, B., Oppacher, F.: ASGA: Improving the ant system by integration with genetic algorithms. In: Proceedings of the 3rd Genetic Programming Conference, pp. 610–617. Morgan Kaufmann, San Francisco (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jorge Tavares
    • 1
  • Francisco B. Pereira
    • 1
    • 2
  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal
  2. 2.ISECCoimbraPortugal

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