Behavioural GP diversity for adaptive stock selection

ABSTRACT

We present a new mechanism for preserving phenotypic behavioural diversity in Genetic Programming. We provide a real-world case study for hedge fund portfolio optimization, and experimental results on real-world data that indicate the importance of phenotypic behavioural diversity both in achieving higher fitness and in improving the robustness of the GP population for continuous learning.

REFERENCES

• 1: Markus Brameier , Wolfgang Banzhaf, Explicit Control of Diversity and Effective Variation Distance in Linear Genetic Programming, Proceedings of the 5th European Conference on Genetic Programming, p.37-49, April 03-05, 2002
• 2: Edmund K. Burke , Steven M. Gustafson , Graham Kendall , Natalio Krasnogor, Advanced Population Diversity Measures in Genetic Programming, Proceedings of the 7th International Conference on Parallel Problem Solving from Nature, p.341-350, September 07-11, 2002
• 3: H. Cobb. An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Technical report, Naval Research Laboratory, 1990.
• 4: M.H. Dempster and C.M. Jones. A real-time adaptive trading system using genetic programming. Quantitative Finance, Vol 1, 2001.
• 5: Anikó Ekárt , Sandor Z. Németh, Maintaining the Diversity of Genetic Programs, Proceedings of the 5th European Conference on Genetic Programming, p.162-171, April 03-05, 2002
• 6: David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1989
• 7: J. Grefenstette. Genetic algorithms for changing environments. In Parallel Problem Solving from Nature 2, pages 137--144. North Holland, 1992.
• 8: C. Karr. Genetic algorithms and fuzzy logic for adaptive process control. In Intelligent Hybrid Systems, Wiley, 1995.
• 9: R. Keller and W. Banzhaf. Explicit maintenance of genetic diversity on genospaces. Unpublished Manuscript, 1994.
• 10: Shane Legg , Marcus Hutter, Fitness uniform deletion: a simple way to preserve diversity, Proceedings of the 2005 conference on Genetic and evolutionary computation, June 25-29, 2005, Washington DC, USA [doi>10.1145/1068009.1068216]
• 11: P. Monsieur and E. Flerackers. Increasing the diversity of a population in genetic programming. In Proc. GECCO'01, page 185. Morgan Kaufmann, 2001.
• 12: C. Reeves and H. Karata. Dynamic sequencing of a multi-processor system: a genetic algorithm approach. In Artificial Neural Nets and Genetic Algorithms. Springer, 1993.
• 13: J. Rosca. Entropy-driven adaptive representation. In Proc. Workshop Genetic Programming: From Theory to Real-World Applications, pages 23--32, 1995.
• 14: C. Schoreels and J.M. Garibaldi. The effect of varying parameter on performance for adaptive agents in techical equity market trading. In Proc. ICCC, 2005.
• 15: W.F. Sharpe. The sharpe ratio. J. Portfolio Management, 21:49--58, 1994.
• 16: Rasmus K. Ursem, Diversity-Guided Evolutionary Algorithms, Proceedings of the 7th International Conference on Parallel Problem Solving from Nature, p.462-474, September 07-11, 2002
• 17: J. Herrmann. A genetic algorithm for minmax optimisation problems. Proc. CEC'99, vol. 2, pages 1099--1103. IEEE, 1999.
• 18: J.F. Miller. Evolving a self-repairing, self-regulating, french flag organism. LNCS, 3102:129--139, 2004.
• 19: F.W. Moore and O.N. Gacia. A new methodology for reducing brittleness in genetic programming. In Proc. NAECON'97, 1997.
• 20: V. Nissen and J. Propach. On the robustness of population-based versus point-based optimisation in the presence of noise. IEEE Transactions on Evolutionary Computation, 2(3), 1998.
• 21: T. Soule. Operator choice and the evolution of robust solutions. In Genetic Programming Theory and Practice, Chapter 16, pages 257--270. Kluwer, 2003.
• 22: Wei Yan , Christopher D. Clack, Evolving robust GP solutions for hedge fund stock selection in emerging markets, Proceedings of the 9th annual conference on Genetic and evolutionary computation, July 07-11, 2007, London, England [doi>10.1145/1276958.1277384]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.m Miscellaneous

General Terms:
Algorithms, Experimentation

Keywords:
diversity, dynamic environment, finance, genetic programming, phenotype, robustness