Parameter Adaptation for GP Forecasting Applications

School of Computer Science, University of Adelaide, Adelaide, SA 5005,Australia, Institute of Computer Science, Polish Academy of Sciences, ul.Ordona 21, 01-237 Warsaw, Poland, and Polish-Japanese Institute ofInformation Technology, ul. Koszykowa 86, 02-008 Warsaw, Polandzbyszek@cs.adelaide.edu.auSummary. Genetic Programming (GP) has been applied to time series forecast-ing often with favorable results. However, for forecasting tasks several open issuesconcerning parameter settings exist. Many real-world forecasting tasks are dynamicin nature and, thus, static parameter settings may lead to inferior performance.This paper presents the results of recent studies investigating non-static parametersettings that are controlled by feedback from the GP search process. Specifically,non-static settings for population size and training data size are explored.

[1]  William B. Langdon,et al.  Some Considerations on the Reason for Bloat , 2002, Genetic Programming and Evolvable Machines.

[2]  陳樹衡,et al.  Option Pricing with Genetic Programming , 1998 .

[3]  Shu-Heng Chen,et al.  Toward a computable approach to the efficient market hypothesis: An application of genetic programming , 1995 .

[4]  Kazuto Yukita,et al.  Study of daily peak load forecasting by structured representation on genetic algorithms for function fitting , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[5]  Peter J. Angeline,et al.  Genetic programming and emergent intelligence , 1994 .

[6]  Rajkumar Venkatesan,et al.  A genetic algorithms approach to growth phase forecasting of wireless subscribers , 2002 .

[7]  Lance D. Chambers.  Practical handbook of genetic algorithms , 1995 .

[8]  M. A. Kaboudan,et al.  Genetic evolution of regression models for business and economic forecasting , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[9]  Mak A. Kaboudan,et al.  Forecasting with computer-evolved model specifications: a genetic programming application , 2003, Comput. Oper. Res..

[10]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[11]  Y. J. Ju,et al.  Genetic-based fuzzy models: Interest rate forecasting problem , 1997 .

[12]  Zbigniew Michalewicz,et al.  Time Series Forecasting for Dynamic Environments: The DyFor Genetic Program Model , 2007, IEEE Transactions on Evolutionary Computation.

[13]  M. Andrews,et al.  Genetic programming for the acquisition of double auction market strategies , 1994 .

[14]  M. A. Kaboudan,et al.  Genetically evolved models and normality of their fitted residuals , 2001 .

[15]  Efraim Turban,et al.  Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real-World Performance , 1992 .

[16]  Bongju Jeong,et al.  A computerized causal forecasting system using genetic algorithms in supply chain management , 2002, J. Syst. Softw..

[17]  M. Kaboudan.  Genetic Programming Prediction of Stock Prices , 2000 .

[18]  Mak A. Kaboudan,et al.  Forecasting Stock Returns Using Genetic Programming in C++ , 1998, FLAIRS.

[19]  Neal Wagner,et al.  Genetic Programming with Efficient Population Control for Financial Time Series Prediction , 2005 .

[20]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[21]  Riccardo Poli,et al.  Fitness Causes Bloat , 1998 .

[22]  Guido Deboeck,et al.  Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets , 1994 .

[23]  R. Savit,et al.  Dynamics of genetic programming and chaotic time series prediction , 1996 .

[24]  J. K. Kinnear,et al.  Advances in Genetic Programming , 1994 .

[25]  Edwin D. de Jong,et al.  Reducing bloat and promoting diversity using multi-objective methods , 2001 .

[26]  V. Avatchanakorn,et al.  Genetic algorithms in forecasting commercial banks deposit , 1997, 1997 IEEE International Conference on Intelligent Processing Systems (Cat. No.97TH8335).

[27]  Zbigniew Michalewicz,et al.  Parameter Control in Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

[28]  M. A. Kaboudan,et al.  A Measure of Time Series’ Predictability Using Genetic Programming , 2004 .

[29]  W. Langdon.  The evolution of size in variable length representations , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[30]  Chulhyun Kim,et al.  Forecasting time series with genetic fuzzy predictor ensemble , 1997, IEEE Trans. Fuzzy Syst..

[31]  Christopher J. Neely,et al.  Predicting Exchange Rate Volatility: Genetic Programming Versus GARCH and RiskMetrics , 2001 .

[32]  Hitoshi Iba,et al.  Genetic programming polynomial models of financial data series , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[33]  Peter Ross,et al.  Small Populations over Many Generations can beat Large Populations over Few Generations in Genetic P , 1997 .

[34]  Dong Gyu Lee,et al.  Genetic programming model for long-term forecasting of electric power demand , 1997 .

[35]  Hitoshi Iba,et al.  Using genetic programming to predict financial data , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[36]  Edward Gately.  Neural networks for financial forecasting , 1995 .

[37]  Francis X. Diebold,et al.  Elements of Forecasting , 1997 .

[38]  Jonas Barklund,et al.  Characterizing Signal Behaviour Using Genetic Programming , 1996, Evolutionary Computing, AISB Workshop.