An Analysis of a Selecto-Lamarckian Model of Multimemetic Algorithms with Dynamic Self-organized Topology

  • Rafael Nogueras
  • Carlos Cotta
  • Carlos M. Fernandes
  • Juan Luis Jiménez Laredo
  • Juan Julián Merelo
  • Agostinho C. Rosa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8273)

Abstract

Multimemetic algorithms (MMAs) are memetic algorithms that explicitly represent and evolve memes (computational representations of problem solving methods) as a part of solutions. We use an idealized selecto-Lamarckian model of MMAs in order to analyze the propagation of memes in spatially structured populations. To this end, we focus on the use of dynamic self-organized spatial structures, based on the stimergic communication among solutions, and compare these with regular static lattices and unstructured (panmictic) populations. An empirical analysis indicates that these dynamic lattices are capable of promoting memetic diversity and provide better results in terms of survival of high-quality memes.

Keywords

Memetic algorithms spatial structure self-organization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alba, E., Dorronsoro, B.: The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Transactions on Evolutionary Computation 9, 126–142 (2005)CrossRefGoogle Scholar
  2. 2.
    Alba, E., Luque, G.: Growth curves and takeover time in distributed evolutionary algorithms. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 864–876. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Castillo, P., Arenas, M.G., Castellano, J.G., Merelo, J.J., Prieto, A., Rivas, R.: Lamarckian evolution and the Baldwin effect in evolutionary neural networks. In: Alba, E., et al. (eds.) AEB 2002, pp. 284–293. Universidad de Extremadura (2002)Google Scholar
  4. 4.
    Dawkins, R.: The Selfish Gene. Clarendon Press, Oxford (1976)Google Scholar
  5. 5.
    Fernandes, C.M., Merelo, J., Cotta, C., Rosa, A.: Towards a 2-dimensional self-organized framework for structured population-based metaheuristics. In: 2012 International Conference on Complex Systems, pp. 1–6. IEEE Press (2012)Google Scholar
  6. 6.
    Fernandes, C.M., Guervós, J.J.M., Laredo, J.L.J., Cotta, C., Rosa, A.C.: Partially connected topologies for particle swarm. In: Blum, C., Alba, E. (eds.) GECCO 2013 (Companion), pp. 11–12. ACM (2013)Google Scholar
  7. 7.
    Fernandes, C.M., Rosa, A.C., Laredo, J.L., Cotta, C., Merelo, J.J.: A study on time-varying partially connected topologies for the particle swarm. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 2450–2456 (2013)Google Scholar
  8. 8.
    Giacobini, M., Tomassini, M., Tettamanzi, A., Alba, E.: Selection intensity in cellular evolutionary algorithms for regular lattices. IEEE Transactions on Evolutionary Computation 9(5), 489–505 (2005)CrossRefGoogle Scholar
  9. 9.
    Hart, W.E., Krasnogor, N., Smith, J.E.: Memetic Evolutionary Algorithms. In: Hart, W.E., Smith, J., Krasnogor, N. (eds.) Recent Advances in Memetic Algorithms. STUDFUZZ, vol. 166, pp. 3–27. Springer-, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Krasnogor, N., Blackburne, B., Burke, E., Hirst, J.: 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)CrossRefGoogle Scholar
  11. 11.
    Krasnogor, N., Gustafson, S.: A study on the use of “self-generation” in memetic algorithms. Natural Computing 3(1), 53–76 (2004)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Laredo, J.L.J., Castillo, P.A., Mora, A.M., Merelo, J.J., Fernandes, C.M.: Resilience to churn of a peer-to-peer evolutionary algorithm. International Journal of High Performance Systems Architecture 1(4), 260–268 (2008)CrossRefGoogle Scholar
  13. 13.
    Laredo, J.L.J., Eiben, A.E., van Steen, M., Merelo, J.J.: Evag: A scalable peer-to-peer evolutionary algorithm. Genetic Programming and Evolvable Machines 11(2), 227–246 (2010)CrossRefGoogle Scholar
  14. 14.
    Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Tech. Rep. Caltech Concurrent Computation Program, Report. 826, California Institute of Technology, Pasadena, California, USA (1989)Google Scholar
  15. 15.
    Moscato, P.: Memetic algorithms: A short introduction. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization. Mcgraw-Hill’s Advanced Topics In Computer Science Series, pp. 219–234. McGraw-Hill, London (1999)Google Scholar
  16. 16.
    Moscato, P., Cotta, C.: A gentle introduction to memetic algorithms. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 57, pp. 105–144. Kluwer Academic Press, New York (2003)Google Scholar
  17. 17.
    Neri, F., Cotta, C., Moscato, P.: Handbook of Memetic Algorithms. SCI, vol. 379. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  18. 18.
    Nogueras, R., Cotta, C.: Analyzing meme propagation in multimemetic algorithms: Initial investigations. In: Federated Conference on Computer Science and Information Systems, Cracow, Poland (forthcoming, 2013)Google Scholar
  19. 19.
    Ong, Y.S., Lim, M.H., Chen, X.: Memetic computation-past, present and future. IEEE Computational Intelligence Magazine 5(2), 24–31 (2010)CrossRefGoogle Scholar
  20. 20.
    Rudolph, G., Sprave, J.: A cellular genetic algorithm with self-adjusting acceptance threshold. In: 1st IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, London, UK, pp. 365–372 (1995)Google Scholar
  21. 21.
    Sarma, J., De Jong, K.: An analysis of local selection algorithms in a spatially structured evolutionary algorithm. In: Bäck, T. (ed.) 7th International Conference on Genetic Algorithms, pp. 181–186. Morgan Kaufmann (1997)Google Scholar
  22. 22.
    Whitacre, J.M., Sarker, R.A., Pham, Q.: The self-organization of interaction networks for nature-inspired optimization. IEEE Transactions on Evolutionary Computation 12, 220–230 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rafael Nogueras
    • 1
  • Carlos Cotta
    • 1
  • Carlos M. Fernandes
    • 2
  • Juan Luis Jiménez Laredo
    • 3
  • Juan Julián Merelo
    • 4
  • Agostinho C. Rosa
    • 2
  1. 1.Dept. Lenguajes y Ciencias de la ComputaciónUniversidad de MálagaSpain
  2. 2.Systems and Robotics InstituteTechnical University of LisbonPortugal
  3. 3.University of LuxembourgLuxembourg
  4. 4.Dept. Arquitectura y Tecnología de ComputadoresUniversidad de GranadaSpain

Personalised recommendations