TOPOLOGICAL AND MOTION STRATEGIES FOR CELLULAR MEMETIC TREE-BASED PORTFOLIO OPTIMIZATION

Portfolio Optimization (PO) is a resource allocation problem where real valued weights are assigned to multiple financial assets in order to maximize the return and minimize the risk. The Memetic Tree-based Algorithm (MTGA), employing a tree representation allied with local search (LS) has shown great performance compared to other weight balancing techniques. In this work, we hybridize MTGA with topological frameworks — Cellular Memetic Algorithms (CMA) — and study four implementations, varying whether the individuals move through the grid, and whether meta-parameters are spread along the axes for self-adaptation. The approaches are compared using a historical data simulation. A CMA which incorporates motion performs best, while parameter tuning was less successful. The results not only describe an improved method for PO, but also have broader implications for cellular models wherein the benefits of motion are shown to deserve further investigation.

[1]  Hitoshi Iba,et al.  The Memetic Tree-based Genetic Algorithm and its application to Portfolio Optimization , 2009, Memetic Comput..

[2]  Ronald Hochreiter,et al.  An Evolutionary Computation Approach to Scenario-Based Risk-Return Portfolio Optimization for General Risk Measures , 2009, EvoWorkshops.

[3]  Mitsuo Gen,et al.  An Effective Decision-Based Genetic Algorithm Approach to Multiobjective Portfolio Optimization Problem , 2007 .

[4]  Piotr Lipinski,et al.  Building Risk-Optimal Portfolio Using Evolutionary Strategies , 2007, EvoWorkshops.

[5]  Tanja Neumann.  Mean Variance Analysis In Portfolio Choice And Capital Markets , 2016 .

[6]  Christopher D. Clack,et al.  Multiobjective robustness for portfolio optimization in volatile environments , 2008, GECCO '08.

[7]  Keshav P. Dahal,et al.  Portfolio optimization using multi-obj ective genetic algorithms , 2007, 2007 IEEE Congress on Evolutionary Computation.

[8]  Carlos R. B. Azevedo,et al.  Terrain-Based Memetic Algorithms for Vector Quantizer Design , 2008, NICSO.

[9]  V. Scott Gordon,et al.  Terrain-Based Genetic Algorithm (TBGA): Modeling Parameter Space as Terrain , 1999, GECCO.

[10]  W. Sharpe,et al.  Mean-Variance Analysis in Portfolio Choice and Capital Markets , 1987 .

[11]  Hitoshi Iba,et al.  Accelerating Differential Evolution Using an Adaptive Local Search , 2008, IEEE Transactions on Evolutionary Computation.

[12]  Carlos R. B. Azevedo,et al.  Adaptive terrain-based memetic algorithms , 2009, GECCO.

[13]  T. Krink,et al.  Parameter control using the agent based patchwork model , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[14]  Ruhul A. Sarker,et al.  An agent-based memetic algorithm (AMA) for solving constrained optimazation problems , 2007, 2007 IEEE Congress on Evolutionary Computation.

[15]  Natalio Krasnogor,et al.  Adaptive Cellular Memetic Algorithms , 2009, Evolutionary Computation.

[16]  L. Darrell Whitley,et al.  Serial and Parallel Genetic Algorithms as Function Optimizers , 1993, ICGA.

[17]  Enrique Alba,et al.  Hierarchical Cellular Genetic Algorithm , 2006, EvoCOP.