Abstract
We consider the issue of intensification/diversification balance in the context of a memetic algorithm for the multiobjective optimization of investment portfolios with cardinality constraints. We approach this issue in this work by considering the selective application of knowledge-augmented operators (local search and a memory of elite solutions) based on the search epoch in which the algorithm finds itself, hence alternating between unbiased search (guided uniquely by the built-in search mechanics of the algorithm) and focused search (intensified by the use of the problem-aware operators). These operators exploit Sharpe index (a measure of the relationship between return and risk) as a source of problem knowledge. We have conducted a sensibility analysis to determine in which phases of the search the application of these operators leads to better results. Our findings indicate that the resulting algorithm is quite robust in terms of parameterization from the point of view of this problem-specific indicator. Furthermore, it is shown that not only can other non-memetic counterparts be outperformed, but that there is a range of parameters in which the MA is also competitive when not better in terms of standard multiobjective performance indicators.
This work is supported by Spanish Ministry of Economy under project Bio4Res (PID2021-125184NB-I00 – http://bio4res.lcc.uma.es) and by Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech.
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Notes
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The data is also available in our group’s repository, at https://osf.io/wg7mn/.
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- 3.
Statistical significance is here determined with the Mann-Whitney U test [12] at the significance level \(\alpha \) indicated in each case.
References
Bader, J., Deb, K., Zitzler, E.: Faster hypervolume-based search using monte carlo sampling. In: Ehrgott, M., et al. (eds.) Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems. Lecture Notes in Economics and Mathematical Systems, vol. 634, pp. 313–326. Springer, Berlin, Heidelberg (2010)
Bader, J., Zitzler, E.: HypE: an algorithm for fast hypervolume-based many-objective optimization. Evol. Comput. 19(1), 45–76 (2011)
Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA–a platform and programming language independent interface for search algorithms. In: Fonseca, C.M., et al. (eds.) Conference on Evolutionary Multi-Criterion Optimization (EMO 2003). Lecture Notes in Computer Science, vol. 2632, pp. 494–508. Springer, Berlin, Heidelberg (2003)
Colombian Stock Market, B.: Variable income market (2008). https://www.bvc.com.co/pps/tibco/portalbvc. Accessed Nov 2017
Colomine Duran, F.E., Cotta, C., Fernández-Leiva, A.J.: A comparative study of multi-objective evolutionary algorithms to optimize the selection of investment portfolios with cardinality constraints. In: Di Chio, C., et al. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 165–173. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29178-4_17
Colomine Durán, F., Cotta, C., Fernández-Leiva, A.J.: Sensitivity to partial lamarckism in a memetic algorithm for constrained portfolio optimization. In: Mora, A. (ed.) Evostar 2021 Late-Breaking Abstracts, arXiv:2106.11804. pp. 9–12 (2021)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Deb, K., Beyer, H.G.: Self-adaptive genetic algorithms with simulated binary crossover. Evol. Comput. 9(2), 197–221 (2001)
Deb, K., Deb, D.: Analysing mutation schemes for real-parameter genetic algorithms. Int. J. Artif. Intell. Soft Comput. 4(1), 1–28 (2014)
Jin, M., Li, Z., Yuan, S.: Research and analysis on markowitz model and index model of portfolio selection. In: Proceedings of the 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021), pp. 1142–1150. Atlantis Press (2021)
Jorion, P.: Value at Risk: The New Benchmark for Managing Financial Risk. In: MacGraw-Hill International Editions: Finance series, McGraw-Hill (2001)
Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18(1), 50–60 (1947)
Markowitz, H.M.: Portfolio selection. J. Finan. 7, 77–91 (1952)
Moscato, P., Cotta, C.: An accelerated introduction to memetic algorithms. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics. ISORMS, vol. 272, pp. 275–309. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91086-4_9
Neri, F., Cotta, C.: Memetic algorithms and memetic computing optimization: a literature review. Swarm Evol. Comput. 2, 1–14 (2012)
Ponsich, A., Jaimes, A.L., Coello, C.A.C.: A survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications. IEEE Trans. Evol. Comput. 17(3), 321–344 (2013)
Sharpe, W.F.: Mutual fund performance. J. Bus. 39, 119–138 (1966)
Veldhuizen, D.A.V., Lamont, G.B.: Multiobjective evolutionary algorithms: analyzing the state-of-the-art. Evol. Comput. 8(2), 125–147 (2000)
Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(67), 67–82 (1997)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou, K.C., et al. (eds.) Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, pp. 95–100. International Center for Numerical Methods in Engineering (Cmine), Athens, Greece (2001)
Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Yao, X., et al. (eds.) Indicator-based selection in multiobjective search. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30217-9_84
Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms—a comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056872
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Colomine Durán, F., Cotta, C., Fernández-Leiva, A.J. (2023). Epoch-Based Application of Problem-Aware Operators in a Multiobjective Memetic Algorithm for Portfolio Optimization. In: Correia, J., Smith, S., Qaddoura, R. (eds) Applications of Evolutionary Computation. EvoApplications 2023. Lecture Notes in Computer Science, vol 13989. Springer, Cham. https://doi.org/10.1007/978-3-031-30229-9_14
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