Lamarckian and Lifelong Memetic Search in Agent-Based Computing

  • Wojciech Korczynski
  • Marek Kisiel-Dorohinicki
  • Aleksander Byrski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10199)

Abstract

Memetic algorithms when used with care can help in balancing exploitation and exploration of the metaheuristics, without the overhead measured by the rapidly increased number of function fitness calls. The paper tackles such balancing of use of metaheuristics in an agent-oriented setting. In particular, application of local search during a computing agent’s life is researched. The results shown for selected benchmark functions are presented along with necessary statistic testing.

Keywords

Memetic algorithms Agent-based computing Continuous optimization Metaheuristics 

Notes

Acknowledgment

This research was supported by AGH University of Science and Technology Statutory Fund no. 11.11.230.124. This research was supported by PlGrid infrastructure.

References

  1. 1.
    Droste, S., Jansen, T., Wegener, I.: Upper and lower bounds for randomized search heuristics in black-box optimization. Theory Comput. Syst. 39, 525–544 (2006)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Michalewicz, Z.: Genetic Algorithms Plus Data Structures Equals Evolution Programs. Springer-Verlag New York, Inc., Secaucus (1994)CrossRefMATHGoogle Scholar
  3. 3.
    Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Technical report Caltech Concurrent Computation Program, Report. 826, California Institute of Technology, Pasadena, California, USA (1989)Google Scholar
  4. 4.
    Eldridge, N., Gould, S.: Punctuated equilibria: an alternative to phyletic gradualism. In: Schopf, T. (ed.) Models in Paleobiology. Freeman, Cooper and Co., San Francisco (1972)Google Scholar
  5. 5.
    Hinton, G., Nolan, S.: How learning can guide evolution. Complex Syst. 1, 495–502 (1987)MATHGoogle Scholar
  6. 6.
    Glover, F.: Scatter search and path relinking. In: New Ideas in Optimization, pp. 297–316. McGraw-Hill Ltd. (1999)Google Scholar
  7. 7.
    Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold Computer Library, New York (1991)Google Scholar
  8. 8.
    Hart, W., Belew, R.: Optimizing an arbitrary function is hard for the genetic algorithm. In: Belew, R., Booker, L. (eds.) Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 190–195. Morgan Kaufmann, San Mateo (1991)Google Scholar
  9. 9.
    Wolpert, D., Macready, W.: No free lunch theorems for search. Technical report SFI-TR-02-010, Santa Fe Institute (1995)Google Scholar
  10. 10.
    Byrski, A.: Agent-Based Metaheuristics in Search and Optimisation. AGH University of Science and Technology Press, Kraków (2013)Google Scholar
  11. 11.
    Kisiel-Dorohinicki, M., Dobrowolski, G., Nawarecki, E.: Agent populations as computational intelligence. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks and Soft Computing, pp. 608–613. Physica, Heidelberg (2003)CrossRefGoogle Scholar
  12. 12.
    Byrski, A.: Tuning of agent-based computing. Comput. Sci. 14(3), 491 (2013)CrossRefGoogle Scholar
  13. 13.
    Wróbel, K., Torba, P., Paszyński, M., Byrski, A.: Evolutionary multi-agent computing in inverse problems. Comput. Sci. 14(3), 367 (2013)CrossRefGoogle Scholar
  14. 14.
    Dreżewski, R., Siwik, L.: Multi-objective optimization technique based on co-evolutionary interactions in multi-agent system. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 179–188. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-71805-5_20Google Scholar
  15. 15.
    Drezewski, R., Siwik, L.: Co-evolutionary multi-agent system for portfolio optimization. In: Brabazon, A., O’Neill, M. (eds.) Natural Computing in Computational Finance. SCI, vol. 1, pp. 271–299. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Krasnogor, N., Smith, J.: A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Trans. Evol. Comput. 9(5), 474–488 (2005)CrossRefGoogle Scholar
  17. 17.
    Moscato, P.: Memetic algorithms: a short introduction. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 219–234. McGraw-Hill, New York City (1999)Google Scholar
  18. 18.
    Moscato, P., Cotta, C.: A modern introduction to memetic algorithms. In: Gendrau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics. International Series in Operations Research and Management Science, vol. 146, 2nd edn., pp. 141–183. Springer, Heidelberg (2010)Google Scholar
  19. 19.
    Korczynski, W., Byrski, A., Kisiel-Dorohinicki, M.: Efficient memetic continuous optimization in agent-based computing. Procedia Comput. Sci. 80, 845–854 (2016). International Conference on Computational Science 2016, ICCS 2016, San Diego, California, USA, 6–8 June 2016CrossRefGoogle Scholar
  20. 20.
    Talbi, E.G.: Metaheuristics: From Design to Implementation. Wiley, Hoboken (2009)CrossRefMATHGoogle Scholar
  21. 21.
    Cetnarowicz, K., Kisiel-Dorohinicki, M., Nawarecki, E.: The application of evolution process in multi-agent world (MAW) to the prediction system. In: Tokoro, M. (ed.) Proceedings of the 2nd International Conference on Multi-Agent Systems (ICMAS 1996), AAAI Press (1996)Google Scholar
  22. 22.
    Byrski, A., Korczynski, W., Kisiel-Dorohinicki, M.: Memetic multi-agent computing in difficult continuous optimisation. In: KES-AMSTA, pp. 181–190 (2013)Google Scholar
  23. 23.
    Cantú-Paz, E.: A summary of research on parallel genetic algorithms. IlliGAL Report No. 95007. University of Illinois (1995)Google Scholar
  24. 24.
    Byrski, A., Schaefer, R., Smołka, M.: Asymptotic guarantee of success for multi-agent memetic systems. Bull. Pol. Acad. Sci.-Tech. Sci. 61(1), 257–278 (2013)Google Scholar
  25. 25.
    Byrski, A., Schaefer, R.: Formal model for agent-based asynchronous evolutionary computation. In: 2009 IEEE Congress on Evolutionary Computation, pp. 78–85, May 2009Google Scholar
  26. 26.
    Schaefer, R., Byrski, A., Smolka, M.: The island model as a markov dynamic system. Int. J. Appl. Math. Comput. Sci. 22(4), 971–984 (2012)MathSciNetCrossRefMATHGoogle Scholar
  27. 27.
    Syswerda, G.: A study of reproduction in generational and steady state genetic algorithms. Found. Genet. Algorithms 2, 94–101 (1991)Google Scholar
  28. 28.
    Gallardo, J.E., Cotta, C., Fernández, A.J.: Finding low autocorrelation binary sequences with memetic algorithms. Appl. Soft Comput. 9(4), 1252–1262 (2009)CrossRefGoogle Scholar
  29. 29.
    Kaziród, M., Korczynski, W., Byrski, A.: Agent-oriented computing platform in python. In: 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 3, pp. 365–372. IEEE (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wojciech Korczynski
    • 1
  • Marek Kisiel-Dorohinicki
    • 1
  • Aleksander Byrski
    • 1
  1. 1.Department of Computer ScienceAGH University of Science and TechnologyKrakowPoland

Personalised recommendations