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

  • S.I.: Advances in Theoretical and Applied Combinatorial Optimization
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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.

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References

  • Bertsimas, D., & Pachamanova, D. (2008). Robust multi period portfolio management in the presence of transaction costs. Computers and Operations Research, 35(2008), 3–17.

    Article  Google Scholar 

  • Bird, R., & Tippett, M. (1986). Naive diversification and portfolio risk-a note. Management Science, 32(2), 244–251.

    Article  Google Scholar 

  • Brands, S., & Gallagher, D. R. (2005). Portfolio selection, diversification and fund-of-funds: A note. Accounting and Finance, 45(2), 185–197.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Carlsson, C., & Fuller, R. (2001). On possibilistic mean value and variance of fuzzy numbers. Fuzzy Sets and Systems, 122(2), 315–326.

    Article  Google Scholar 

  • 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.

  • Chen, W. (2015). Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem. Physica A, 429(2015), 125–139.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Doganoglu, T., Hartz, C., & Mittnik, S. (2007). Portfolio optimization when risk factors are conditionally varying and heavy tailed. Computational Economics, 29(3), 333–354.

    Article  Google Scholar 

  • 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 

  • Fabozzi, F. J., Huang, D., & Zhou, G. (2010). Robust portfolios: Contributions from operations research and finance. Annals of Operations Research, 176(1), 191–220.

    Article  Google Scholar 

  • Fielitz, B. D. (1974). Indirect versus direct diversification. Financial Management, 3(4), 54–62.

    Article  Google Scholar 

  • Geyer, A., Hanke, M., & Weissensteiner, A. (2009). A stochastic programming approach for multi-period portfolio optimization. Computational Management Science, 6(2009), 187–208.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Book  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Huang, X. (2011). Minimax mean-variance models for fuzzy portfolio selection. Soft Computing, 15(2011), 251–260.

    Article  Google Scholar 

  • Huang, X. (2012). An entropy method for diversified fuzzy portfolio selection. International Journal of Fuzzy Systems, 14(2012), 160–165.

    Google Scholar 

  • Jennings, E. H. (1971). An empirical analysis of some aspects of common stock diversification. Journal of Financial and Quantitative Analysis, 6(02), 797–813.

    Article  Google Scholar 

  • Johnson, K. H., & Shannon, D. S. (1974). A note on diversification and the reduction of dispersion. Journal of Financial Economics, 1(4), 365–372.

    Article  Google Scholar 

  • Krzemienowski, A. (2009). Risk preference modeling with conditional average: An application to portfolio optimization. Annals of Operations Research, 165(1), 67–95.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Li, D., & Ng, W. L. (2000). Optimal dynamic portfolio selection: Multiperiod mean-variance formulation. Mathematical Finance, 10(2000), 387–406.

    Article  Google Scholar 

  • Li, X., Shou, B., & Qin, Z. (2012). An expected regret minimization portfolio selection model. European Journal of Operational Research, 218(2012), 484–492.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Liu, Y. J., & Zhang, W. G. (2013). Fuzzy portfolio optimization model under real constraints. Insurance: Mathematics and Economics, 53(2013), 704–711.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77–91.

    Google Scholar 

  • Mehlawat, M. K. (2016). Credibilistic mean-entropy models for multi-period portfolio selection with multi-choice aspiration levels. Information Sciences, 345(2016), 9–26.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Metaxiotis, K., & Liagkouras, K. (2012). Multiobjective evolutionary algorithms for portfolio management: A comprehensive literature review. Expert Systems with Applications, Elsevier, 39(14), 11685–11698.

    Article  Google Scholar 

  • Mulvey, J. M., & Vladimirou, H. (1992). Stochastic network programming for financial planning problems. Management Science, 38(11), 1642–1664.

    Article  Google Scholar 

  • Rudolf, M., & Ziemba, W. T. (2004). Intertemporal surplus management. Journal of Economic Dynamics and Control, 28(5), 975–990.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Saeidifar, A., & Pasha, E. (2009). The possibilistic moments of fuzzy numbers and their applications. Journal of Computational and Applied Mathematics, 223(2), 1028–1042.

    Article  Google Scholar 

  • Solnik, B. H. (1974). Why not diversify internationally rather than domestically? Financial Analysts Journal, 30(4), 48–52+54.

    Article  Google Scholar 

  • 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).

  • Tang, G. Y. N. (2004). How efficient is naive portfolio diversification? An educational note. Omega, 32(2), 155–160.

    Article  Google Scholar 

  • Vercher, E., Bermudez, J., & Segura, J. (2007). Fuzzy portfolio optimization under downside risk measures. Fuzzy Sets and Systems, 158(2007), 769–782.

    Article  Google Scholar 

  • 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 

  • Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1978), 3–28.

    Article  Google Scholar 

  • Zenios, S. A. (2007). Practical financial optimization: Decision making for financial engineers. New York: Wiley.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Zenios, S. A., & Kang, P. (1993). Mean-absolute deviation portfolio optimization for mortgage-backed securities. Annals of Operations Research, 45(1), 433–450.

    Article  Google Scholar 

  • Zhang, Q., & Li, H. (2007). MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712–731.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2), 173–195.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

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  1. Decision Support Systems Laboratory, Department of Informatics, University of Piraeus, 80, Karaoli and Dimitriou Str., 18534, Piraeus, Greece

    K. Liagkouras & K. Metaxiotis

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  1. K. Liagkouras
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  2. K. Metaxiotis
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Correspondence to K. Liagkouras.

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Liagkouras, K., Metaxiotis, K. Improving the performance of evolutionary algorithms: a new approach utilizing information from the evolutionary process and its application to the fuzzy portfolio optimization problem. Ann Oper Res 272, 119–137 (2019). https://doi.org/10.1007/s10479-018-2876-1

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