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Annals of Operations Research

, Volume 272, Issue 1–2, pp 119–137 | Cite as

Improving the performance of evolutionary algorithms: a new approach utilizing information from the evolutionary process and its application to the fuzzy portfolio optimization problem

  • K. LiagkourasEmail author
  • K. Metaxiotis
S.I.: Advances in Theoretical and Applied Combinatorial Optimization
  • 83 Downloads

Abstract

This paper examines the incorporation of useful information extracted from the evolutionary process, in order to improve algorithm performance. In order to achieve this objective, we introduce an efficient method of extracting and utilizing valuable information from the evolutionary process. Finally, this information is utilized for optimizing the search process. The proposed algorithm is compared with the NSGAII for solving some real-world instances of the fuzzy portfolio optimization problem. The proposed algorithm outperforms the NSGAII for all examined test instances.

Keywords

Information Fuzzy systems Portfolio optimization Evolutionary algorithms 

References

  1. Bertsimas, D., & Pachamanova, D. (2008). Robust multi period portfolio management in the presence of transaction costs. Computers and Operations Research, 35(2008), 3–17.Google Scholar
  2. Bird, R., & Tippett, M. (1986). Naive diversification and portfolio risk-a note. Management Science, 32(2), 244–251.Google Scholar
  3. Brands, S., & Gallagher, D. R. (2005). Portfolio selection, diversification and fund-of-funds: A note. Accounting and Finance, 45(2), 185–197.Google Scholar
  4. Calvo, C., Ivorra, C., & Liern, V. (2016). Fuzzy portfolio selection with non-financial goals: Exploring the efficient frontier. Annals of Operations Research, 245(1), 31–46.Google Scholar
  5. Carlsson, C., & Fuller, R. (2001). On possibilistic mean value and variance of fuzzy numbers. Fuzzy Sets and Systems, 122(2), 315–326.Google Scholar
  6. Cesarone, F., Scozzari, A., & Tardella, F. (2008). Efficient algorithms for mean–variance portfolio optimization with hard real-world constraints. In The 18th AFIR colloquium: Financial risk in a changing world, Rome, September 30–October 3, 2008.Google Scholar
  7. Chen, W. (2015). Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem. Physica A, 429(2015), 125–139.Google Scholar
  8. Chen, Z. P. (2005). Multiperiod consumption and portfolio decisions under the multivariate GARCH model with transaction costs and CVaR-based risk control. OR Spectrum, 27(2005), 603–632.Google Scholar
  9. Chen, W., Gai, Y., & Gupta, P. (2017). Efficiency evaluation of fuzzy portfolio in different risk measures via DEA. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2411-9.Google Scholar
  10. Chen, W., Wang, Y., & Mehlawat, M. K. (2016). A hybrid FA–SA algorithm for fuzzy portfolio selection with transaction costs. Annals of Operations Research.  https://doi.org/10.1007/s10479-016-2365-3.Google Scholar
  11. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA, II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.Google Scholar
  12. Deb, K., & Tiwari, S. (2008). Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization. European Journal of Operational Research, 185(3), 1062–1087.Google Scholar
  13. Doganoglu, T., Hartz, C., & Mittnik, S. (2007). Portfolio optimization when risk factors are conditionally varying and heavy tailed. Computational Economics, 29(3), 333–354.Google Scholar
  14. Evans, J. L., & Archer, S. H. (1968). Diversification and the reduction of dispersion: An empirical analysis. Journal of Finance, 23(5), 761–767.Google Scholar
  15. Fabozzi, F. J., Huang, D., & Zhou, G. (2010). Robust portfolios: Contributions from operations research and finance. Annals of Operations Research, 176(1), 191–220.Google Scholar
  16. Fielitz, B. D. (1974). Indirect versus direct diversification. Financial Management, 3(4), 54–62.Google Scholar
  17. Geyer, A., Hanke, M., & Weissensteiner, A. (2009). A stochastic programming approach for multi-period portfolio optimization. Computational Management Science, 6(2009), 187–208.Google Scholar
  18. Gupta, P., Inuiguchi, M., Mehlawat, M. K., & Mittal, G. (2013a). Multiobjective credibilistic portfolio selection model with fuzzy chance—constraints. Information Sciences, 229(2013), 1–17.Google Scholar
  19. Gupta, P., Mehlawat, M. K., Inuiguchi, M., & Chandra, S. (2014). Fuzzy portfolio optimization: Advances in hybrid multi-criteria methodologies, studies in fuzziness and soft computing (Vol. 316). Heidelberg: Springer.Google Scholar
  20. Gupta, P., Mittal, G., & Mehlawat, M. K. (2013b). Expected value multiobjective portfolio rebalancing model with fuzzy parameters. Insurance: Mathematics and Economics, 52(2013), 190–203.Google Scholar
  21. He, W., Chen, Y., & Yin, Z. (2016a). Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Transactions on Cybernetics, 46(3), 620–629.  https://doi.org/10.1109/TCYB.2015.2411285.Google Scholar
  22. He, W., Dong, Y., & Sun, C. (2016b). Adaptive neural impedance control of a robotic manipulator with input saturation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(3), 334–344.  https://doi.org/10.1109/TSMC.2015.2429555.Google Scholar
  23. He, W., Ouyang, Y., & Hong, J. (2017a). Vibration control of a flexible robotic manipulator in the presence of input deadzone. IEEE Transactions on Industrial Informatics, 13(1), 48–59.  https://doi.org/10.1109/TII.2016.2608739.Google Scholar
  24. He, W., Yin, Z., & Sun, C. (2017b). Adaptive neural network control of a marine vessel with constraints using the asymmetric barrier Lyapunov function. IEEE Transactions on Cybernetics, 47(7), 1641–1651.  https://doi.org/10.1109/TCYB.2016.2554621.Google Scholar
  25. Huang, X. (2011). Minimax mean-variance models for fuzzy portfolio selection. Soft Computing, 15(2011), 251–260.Google Scholar
  26. Huang, X. (2012). An entropy method for diversified fuzzy portfolio selection. International Journal of Fuzzy Systems, 14(2012), 160–165.Google Scholar
  27. Jennings, E. H. (1971). An empirical analysis of some aspects of common stock diversification. Journal of Financial and Quantitative Analysis, 6(02), 797–813.Google Scholar
  28. Johnson, K. H., & Shannon, D. S. (1974). A note on diversification and the reduction of dispersion. Journal of Financial Economics, 1(4), 365–372.Google Scholar
  29. Krzemienowski, A. (2009). Risk preference modeling with conditional average: An application to portfolio optimization. Annals of Operations Research, 165(1), 67–95.Google Scholar
  30. Krzemienowski, A., & Szymczyk, S. (2016). Portfolio optimization with a copula-based extension of conditional value-at-risk. Annals of Operations Research, 237(1), 219–236.Google Scholar
  31. Li, D., & Ng, W. L. (2000). Optimal dynamic portfolio selection: Multiperiod mean-variance formulation. Mathematical Finance, 10(2000), 387–406.Google Scholar
  32. Li, X., Shou, B., & Qin, Z. (2012). An expected regret minimization portfolio selection model. European Journal of Operational Research, 218(2012), 484–492.Google Scholar
  33. Liagkouras, K., & Metaxiotis, K. (2014). A new probe guided mutation operator and its application for solving the cardinality constrained portfolio optimization problem. Expert Systems with Applications, 41(2014), 6274–6290.Google Scholar
  34. Liagkouras, K., & Metaxiotis, K. (2015a). Efficient portfolio construction with the use of multiobjective evolutionary algorithms: Best practices and performance metrics. International Journal of Information Technology and Decision Making, 14(3), 535–564.Google Scholar
  35. Liagkouras, K., & Metaxiotis, K. (2015b). An experimental analysis of a new two-stage crossover operator for multiobjective optimization. Soft computing, 21, 1–31.  https://doi.org/10.1007/s00500-015-1810-6.Google Scholar
  36. Liagkouras, K., & Metaxiotis, K. (2016a). A probe guided crossover operator for more efficient exploration of the search space. In G. A. Tsihrintzis, M. Virvou & L. Jain (Eds.), Springer, studies in computational intelligence series, advances in knowledge engineering: Paradigms and applications. Springer, studies in computational intelligence series. Berlin, Heidelberg: Springer.Google Scholar
  37. Liagkouras, K., & Metaxiotis, K. (2016b). A new efficiently encoded multiobjective algorithm for the solution of the cardinality constrained portfolio optimization problem. Annals of Operations Research.  https://doi.org/10.1007/s10479-016-2377-z.Google Scholar
  38. Liagkouras, K., & Metaxiotis, K. (2017a). Examining the effect of different configuration issues of the multiobjective evolutionary algorithms on the efficient frontier formulation for the constrained portfolio optimization problem. Journal of the Operational Research Society, 69(3), 416–438.  https://doi.org/10.1057/jors.2016.38.Google Scholar
  39. Liagkouras, K., & Metaxiotis, K. (2017b). Handling the complexities of the multi-constrained portfolio optimization problem with the support of a novel MOEA. Journal of the Operational Research Society, 1–20.Google Scholar
  40. Liagkouras, K., & Metaxiotis, K. (2018). Multi-period mean–variance fuzzy portfolio optimization model with transaction costs. Engineering Applications of Artificial Intelligence, 67(2018), 260–269.Google Scholar
  41. Liu, Y. J., & Zhang, W. G. (2013). Fuzzy portfolio optimization model under real constraints. Insurance: Mathematics and Economics, 53(2013), 704–711.Google Scholar
  42. Liu, Y. J., Zhang, W. G., & Xu, W. J. (2012). Fuzzy multi-period portfolio selection optimization models using multiple criteria. Automatica, 48(2012), 3042–3053.Google Scholar
  43. Liu, Y. J., Zhang, W. G., & Zhang, Q. (2016). Credibilistic multi-period portfolio optimization model withbankruptcy control and affine recourse. Applied Soft Computing, 38(2016), 890–906.Google Scholar
  44. Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77–91.Google Scholar
  45. Mehlawat, M. K. (2016). Credibilistic mean-entropy models for multi-period portfolio selection with multi-choice aspiration levels. Information Sciences, 345(2016), 9–26.Google Scholar
  46. Messaoudi, L., Aouni, B., & Rebai, A. (2017). Fuzzy chance-constrained goal programming model for multi-attribute financial portfolio selection. Annals of Operations Research, 251, 193.  https://doi.org/10.1007/s10479-015-1937-y.Google Scholar
  47. Metaxiotis, K., & Liagkouras, K. (2012). Multiobjective evolutionary algorithms for portfolio management: A comprehensive literature review. Expert Systems with Applications, Elsevier, 39(14), 11685–11698.Google Scholar
  48. Mulvey, J. M., & Vladimirou, H. (1992). Stochastic network programming for financial planning problems. Management Science, 38(11), 1642–1664.Google Scholar
  49. Rudolf, M., & Ziemba, W. T. (2004). Intertemporal surplus management. Journal of Economic Dynamics and Control, 28(5), 975–990.Google Scholar
  50. Sadjadi, S. J., Seyedhosseini, S. M., & Hassanlou, K. H. (2011). Fuzzy multi period portfolio selection with different rates for borrowing and lending. Applied Soft Computing, 11(2011), 3821–3826.Google Scholar
  51. Saeidifar, A., & Pasha, E. (2009). The possibilistic moments of fuzzy numbers and their applications. Journal of Computational and Applied Mathematics, 223(2), 1028–1042.Google Scholar
  52. Solnik, B. H. (1974). Why not diversify internationally rather than domestically? Financial Analysts Journal, 30(4), 48–52+54.Google Scholar
  53. Streichert, F., Ulmer, H., & Zell, A. (2004). Evaluating a hybrid encoding and three crossover operators on the constrained portfolio selection problem. In Proceedings of the congress on evolutionary computation (CEC 2004), Portland, Oregon (pp. 932–939).Google Scholar
  54. Tang, G. Y. N. (2004). How efficient is naive portfolio diversification? An educational note. Omega, 32(2), 155–160.Google Scholar
  55. Vercher, E., Bermudez, J., & Segura, J. (2007). Fuzzy portfolio optimization under downside risk measures. Fuzzy Sets and Systems, 158(2007), 769–782.Google Scholar
  56. Wang, Z., & Liu, S. (2013). Multi-period mean–variance portfolio selection with fixed and proportional transaction costs. Journal of Industrial and Management Optimization, 9(2013), 643–657.Google Scholar
  57. Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1978), 3–28.Google Scholar
  58. Zenios, S. A. (2007). Practical financial optimization: Decision making for financial engineers. New York: Wiley.Google Scholar
  59. Zenios, S. A., Holmer, M. R., McKendall, R., & Vassiadou-Zeniou, C. (1998). Dynamic models for fixed-income portfolio management under uncertainty. Journal of Economic Dynamics and Control, 22(10), 1517–1541.Google Scholar
  60. Zenios, S. A., & Kang, P. (1993). Mean-absolute deviation portfolio optimization for mortgage-backed securities. Annals of Operations Research, 45(1), 433–450.Google Scholar
  61. Zhang, Q., & Li, H. (2007). MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712–731.Google Scholar
  62. Zhang, W. G., Liu, Y. J., & Xu, W. J. (2012). A possibilistic mean semivariance-entropy model for multi-period portfolio selection with transaction costs. European Journal of Operational Research, 222(2012), 341–349.Google Scholar
  63. Zhang, W. G., Zhang, X. L., & Xiao, W. L. (2009). Portfolio selection under possibilistic mean-variance utility and a SMO algorithm. European Journal of Operational Research, 197(2009), 693–700.Google Scholar
  64. Zhu, S. X., Li, D., & Wang, S. Y. (2004). Risk control over bankruptcy in dynamic portfolio selection: A generalized mean-variance formulation. IEEE Transactions on Automatic Control, 49(2004), 447–457.Google Scholar
  65. Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2), 173–195.Google Scholar
  66. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., & Da Fonseca, V. G. (2003). Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on Evolutionary Computation, 7(2), 117–132.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Decision Support Systems Laboratory, Department of InformaticsUniversity of PiraeusPiraeusGreece

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