Diverse committees vote for dependable profits

Stock selection for hedge fund portfolios is a challenging problem for Genetic Programming (GP) because the markets (the environment in which the GP solution must survive) are dynamic, unpredictable and unforgiving. How can GP be improved so that solutions are produced that are robust to non-trivial changes in the environment? We explore an approach that uses a voting committee of GP individualswith differing phenotypic behaviour.

[1]  Jürgen Branke,et al.  Creating Robust Solutions by Means of Evolutionary Algorithms , 1998, PPSN.

[2]  Josef Kittler,et al.  Multiple Classifier Systems , 2004, Lecture Notes in Computer Science.

[3]  Byoung-Tak Zhang,et al.  Building Optimal Committees of Genetic Programs , 2000, PPSN.

[4]  Ibrahim Kuscu,et al.  Generalisation and domain specific functions in genetic programming , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[5]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[6]  James P. Egan,et al.  Signal detection theory and ROC analysis , 1975 .

[7]  Terence Soule,et al.  Voting teams: a cooperative approach to non-typical problems using genetic programming , 1999 .

[8]  Frank W. Moore,et al.  A new methodology for reducing brittleness in genetic programming , 1997, Proceedings of the IEEE 1997 National Aerospace and Electronics Conference. NAECON 1997.

[9]  Shigeyoshi Tsutsui,et al.  Genetic algorithms with a robust solution searching scheme , 1997, IEEE Trans. Evol. Comput..

[10]  W. Sharpe.  The Sharpe Ratio , 1994 .

[11]  Chris P. Bowers.  Formation of modules in a computational model of embryogeny , 2005, 2005 IEEE Congress on Evolutionary Computation.

[12]  Mark Herbster,et al.  Tracking the Best Expert , 1995, Machine-mediated learning.

[13]  Masao Nagasaki,et al.  Foundations of Systems Biology - Using Cell Illustrator® and Pathway Databases , 2009, Computational Biology.

[14]  A. Gierer,et al.  Regeneration of hydra from reaggregated cells. , 1972, Nature: New biology.

[15]  Shauna Ferris,et al.  When Genius Failed , 2001 .

[16]  Terence Soule,et al.  Behavioral Diversity and a Probabilistically Optimal GP Ensemble , 2004, Genetic Programming and Evolvable Machines.

[17]  T. Bersano-Begey,et al.  A Discussion on Generality and Robustness and a Framework for Fitness Set Construction in Genetic Programming to Promote Robustness , 1997 .

[18]  Ibrahim Kushchu,et al.  Genetic programming and evolutionary generalization , 2002, IEEE Trans. Evol. Comput..

[19]  David E. Goldberg,et al.  Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise , 1996, Evolutionary Computation.

[20]  J. Fitzpatrick,et al.  Genetic Algorithms in Noisy Environments , 2005, Machine Learning.

[21]  Claudio De Stefano,et al.  Exploiting reliability for dynamic selection of classi .ers by means of genetic algorithms , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[22]  Mark Herbster,et al.  Learning Additive Models Online with Fast Evaluating Kernels , 2001, COLT/EuroCOLT.

[23]  C. Wilke,et al.  Robustness and Evolvability in Living Systems , 2006 .

[24]  Arthur K. Kordon,et al.  Robust Inferential Sensors Based on Ensemble of Predictors Generated by Genetic Programming , 2004, PPSN.

[25]  Thomas Bäck,et al.  Evolution Strategies on Noisy Functions: How to Improve Convergence Properties , 1994, PPSN.

[26]  Xin Yao,et al.  Evolutionary ensembles with negative correlation learning , 2000, IEEE Trans. Evol. Comput..

[27]  Ibrahim Kuscu,et al.  Promoting Generalisation of Learned Behaviours in Genetic Programming , 1998, PPSN.

[28]  Terence Soule.  Heterogeneity and Specialization in Evolving Teams , 2000, GECCO.

[29]  Sean Luke,et al.  Methods for Evolving Robust Programs , 2003, GECCO.

[30]  Julian Francis Miller,et al.  Evolving a Self-Repairing, Self-Regulating, French Flag Organism , 2004, GECCO.

[31]  Xin Yao,et al.  Diversity creation methods: a survey and categorisation , 2004, Inf. Fusion.

[32]  Susana Cecilia Esquivel,et al.  An Evolutionary Algorithm to Track Changes of Optimum Value Locations in Dynamic Environments , 2004 .

[33]  Mu Zhu,et al.  Darwinian Evolution in Parallel Universes: A Parallel Genetic Algorithm for Variable Selection , 2006, Technometrics.

[34]  Mark Herbster,et al.  Tracking the Best Linear Predictor , 2001, J. Mach. Learn. Res..

[35]  Volker Nissen,et al.  On the robustness of population-based versus point-based optimization in the presence of noise , 1998, IEEE Trans. Evol. Comput..

[36]  Terence Soule,et al.  Operator Choice and the Evolution of Robust Solutions , 2003 .

[37]  William B. Langdon,et al.  Data Fusion by Intelligent Classifier Combination , 2001 .

[38]  Vasile Palade,et al.  Multi-Classifier Systems: Review and a roadmap for developers , 2006, Int. J. Hybrid Intell. Syst..