Agent-Based Co-operative Co-evolutionary Algorithms for Multi-objective Portfolio Optimization

  • Rafał Dreżewski
  • Krystian Obrocki
  • Leszek Siwik
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 293)

Summary

Co-evolutionary techniques makes it possible to apply evolutionary algorithms in the cases when it is not possible to formulate explicit fitness function. In the case of social and economic simulations such techniques provide us tools for modeling interactions between social and economic agents-especially when agent-based models of co-evolution are used. In this chapter agent-based versions of multi-objective co-operative co-evolutionary algorithms are presented and applied to portfolio optimization problem. The agent-based algorithms are compared with classical versions of SPEA2 and NSGA2 multi-objective evolutionary algorithms with the use of multi-objective test problems and multi-objective portfolio optimization problem. Presented results show that agent-based algorithms obtain better results in the case of multi-objective test problems, while in the case of portfolio optimization problem results are mixed.

Keywords

Multiobjective Optimization Pareto Frontier Computational Agent SPEA2 Algorithm Warsaw Stock Exchange 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)MATHGoogle Scholar
  2. 2.
    Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multi-objective optimization. Tech. rep., Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology (2001)Google Scholar
  3. 3.
    Dreżewski, R.: A model of co-evolution in multi-agent system. In: Mařík, V., Müller, J.P., Pěchouček, M. (eds.) CEEMAS 2003. LNCS (LNAI), vol. 2691, pp. 314–323. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Dreżewski, R.: Co-evolutionary multi-agent system with speciation and resource sharing mechanisms. Computing and Informatics 25(4), 305–331 (2006)MATHGoogle Scholar
  5. 5.
    Dreżewski, R., Obrocki, K.: Co-operative co-evolutionary approach to multi-objective optimization. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS (LNAI), vol. 5572, pp. 277–284. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Dreżewski, R., Siwik, L.: Co-evolutionary multi-agent system with sexual selection mechanism for multi-objective optimization. In: Proceedings of the IEEE World Congress on Computational Intelligence (WCCI 2006). IEEE press, Los Alamitos (2006a)Google Scholar
  7. 7.
    Dreżewski, R., Siwik, L.: Multi-objective optimization using co-evolutionary multi-agent system with host-parasite mechanism. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3993, pp. 871–878. Springer, Heidelberg (2006b)CrossRefGoogle Scholar
  8. 8.
    Dreżewski, R., Siwik, L.: Agent-based co-operative co-evolutionary algorithm for multi-objective optimization. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 388–397. Springer, Heidelberg (2008a)CrossRefGoogle Scholar
  9. 9.
    Dreżewski, R., Siwik, L.: Co-evolutionary multi-agent system for portfolio optimization. In: Brabazon, A., O’Neill, M. (eds.) Natural Computation in Computational Finance, pp. 271–299. Springer, Heidelberg (2008b)CrossRefGoogle Scholar
  10. 10.
    Dreżewski, R., Sepielak, J., Siwik, L.: Classical and agent-based evolutionary algorithms for investment strategies generation. In: Brabazon, A., O’Neill, M. (eds.) Natural Computation in Computational Finance, vol. 2. Springer, Heidelberg (2009)Google Scholar
  11. 11.
    Iorio, A., Li, X.: A cooperative coevolutionary multiobjective algorithm using non-dominated sorting. In: Deb, K., Poli, R., Banzhaf, W., Beyer, H.G., Burke, E.K., Darwen, P.J., Dasgupta, D., Floreano, D., Foster, J.A., Harman, M., Holland, O., Lanzi, P.L., Spector, L., Tettamanzi, A., Thierens, D., Tyrrell, A.M. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 537–548. Springer, Heidelberg (2004)Google Scholar
  12. 12.
    jAgE—Agent-Based Evolution Platform (2009), http://age.iisg.agh.edu.pl
  13. 13.
    Keerativuttitumrong, N., Chaiyaratana, N., Varavithya, V.: Multi-objective co-operative co-evolutionary genetic algorithm. In: Merelo, J.J., Adamidis, P., Beyer, H.G. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 288–297. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Paredis, J.: Coevolutionary algorithms. In: Bäck, T., Fogel, D., Michalewicz, Z. (eds.) Handbook of Evolutionary Computation, (suppl.1). IOP Publishing/Oxford University Press (1998)Google Scholar
  15. 15.
    Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications. PhD thesis, Swiss Federal Institute of Technology, Zurich (1999)Google Scholar
  16. 16.
    Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2), 173–195 (2000)CrossRefGoogle Scholar
  17. 17.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. Tech. Rep. TIK-Report 103, Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Rafał Dreżewski
    • 1
  • Krystian Obrocki
    • 1
  • Leszek Siwik
    • 1
  1. 1.Department of Computer ScienceAGH University of Science and TechnologyKrakówPoland

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