ABSTRACT
The synthesis of stochastic processes using genetic programming is investigated. Stochastic process behaviours take the form of time series data, in which quantities of interest vary over time in a probabilistic, and often noisy, manner. A suite of statistical feature tests are performed on time series plots from example processes, and the resulting feature values are used as targets during evolutionary search. A process algebra, the stochastic π-calculus, is used to denote processes. Investigations consider variations of GP representations for a subset of the stochastic π-calculus, for example, the use of channel unification, and various grammatical constraints. Target processes of varying complexity are studied. Results show that the use of grammatical GP with statistical feature tests can successfully synthesize stochastic processes. Success depends upon a selection of appropriate feature tests for characterizing the target behaviour, and the complexity of the target process.
- P. Angeline. Evolving Predictors for Chaotic Time Series. In Proc. SPIE: Application and Science of Computational Intelligence, volume 3390, pages 170--180, 1998.Google ScholarCross Ref
- R. Blossey, L. Cardelli, and A. Phillips. A Compositional Approach to the Stochastic Dynamics of Gene Networks. Trans. in Comp. Sys. Bio (TCSB), 3939:99--122, 2006. Google ScholarDigital Library
- A. Borrelli, I. De Falco, A. Della Cioppa, M. Nicodemi, and G. Trautteura. Performance of genetic programming to extract the trend in noisy data series. Physica A: Statistical and Theoretical Physics, 370(1):104--108, 2006.Google ScholarCross Ref
- D.-Y. Cho, K.-H. Cho, and B.-T. Zhang. Identification of biochemical networks by S--tree based genetic programming. Bioinformatics, 22(13):1631--1640, 2006. Google ScholarDigital Library
- D. Chu. Evolving genetic regulatory networks for systems biology. In D. Srinivasan and L. Wang, editors, Proc. CEC 2007, pages 875--882, Singapore, 25--28 Sept. 2007. IEEE Press.Google Scholar
- B. Drennan and R. Beer. Evolution of repressilators using a biologically-motivated model of gene expression. In L. R. et al., editor, Artificial Life X: Proc. Tenth Intl. Conf. on the Simulation and Synthesis of Living Systems, pages 22--27. MIT Press, August 2006.Google Scholar
- D. Gillespie. Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem, 81:2340--2361, 1977.Google ScholarCross Ref
- J. Imada. Evolutionary synthesis of stochastic gene network models using feature--based search spaces. Master's thesis, Department of Computer Science, Brock University, 2009.Google Scholar
- J. Imada and B. Ross. Using Feature-based Fitness Evaluation in Symbolic Regression with Added Noise. In Proc. GECCO 2008 Late Breaking Papers, July 2008. Google ScholarDigital Library
- J. Kitagawa and H. Iba. Identifying Metabolic Pathways and Gene Regulation Networks with Evolutionary Algorithms. In G. Fogel and D. Corne, editors, Evolutionary Computation in Bioinformatics, pages 255--278. Morgan Kaufmann, 2003.Google ScholarCross Ref
- J. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992. Google ScholarDigital Library
- J. Koza, M. Keane, M. Streeter, W. Mydlowec, J. Yu, and G. Lanza. Genetic Programming IV: Routine Human-Competitive Machine Intelligence. Kluwer Academic Publishers, 2003. Google ScholarDigital Library
- A. Leier, P. Kuo, W. Banzhaf, and K. Burrage. Evolving noisy oscillatory dynamics in genetic regulatory networks. In P. C. et al., editor, EuroGP 2006, volume 3905 of LNCS, pages 290--299. Springer, 2006. Google ScholarDigital Library
- H. Liu and H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998. Google ScholarDigital Library
- R. Milner. Communication and Concurrency. Prentice Hall, 1989. Google ScholarDigital Library
- A. Nanopoulos, R. Alcock, and Y. Manolopoulos. Feature-based classification of time-series data. In Information processing and technology, pages 49--61. Nova Science Publishers, Inc., Commack, NY, USA, 2001. Google ScholarDigital Library
- A. Phillips. The stochastic pi machine, 2008. http://research.microsoft.com/ aphillip/spim/. Last accessed Dec 9, 2008.Google Scholar
- A. Phillips and L. Cardelli. A Correct Abstract Machine for the Stochastic Pi-calculus. In Proc. Bioconcur'04, 2004.Google Scholar
- C. Priami. Stochastic pi-Calculus. The Computer Journal, 38(7):579--589, 1995.Google ScholarCross Ref
- C. Priami, A. Regev, E. Shapiro, and W. Silverman. Application of a stochastic name-passing calculus to representation and simulation of molecular processes. Information Processing Letters, 80:25--31, 2001. Google ScholarDigital Library
- R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2007.Google Scholar
- K. Rodriguez-Vazquez and P. J. Fleming. Evolution of mathematical models of chaotic systems based on multiobjective genetic programming. Knowledge and Information Systems, 8(2):235--256, Aug. 2005. Google ScholarDigital Library
- B. Ross. Logic-based Genetic Programming with Definite Clause Translation Grammars. New Generation Computing, 19(4):313--337, 2001. Google ScholarDigital Library
- B. Ross. Using Genetic Programming to Synthesize Monotonic Stochastic Processes. In Proceedings CI-2007, July 2007. Google ScholarDigital Library
- B. Ross and J. Imada. Using Multi-objective Genetic Programming to Synthesize Stochastic Processes. In Genetic Programming -- Theory and Practice, May 2009.Google Scholar
- R. Schwaerzel and T. Bylander. Predicting currency exchange rates by genetic programming with trigonometric functions and high-order statistics. In M. Cattolico, editor, GECCO 2006, pages 955--956. ACM, 2006. Google ScholarDigital Library
- F. Streichert, H. Planatscher, C. Spieth, H. Ulmer, and A. Zell. Comparing genetic programming and evolution strategies on inferring gene regulatory networks. In K. et al., editor, GECCO-2004, volume 3102 of LNCS, pages 471--480, Seattle, WA, 2004. Springer-Verlag.Google Scholar
- S. Strogatz. Nonlinear Dynamics and Chaos. Westview Press, 1994.Google Scholar
- X. Wang, K. Smith, and R. Hyndman. Characteristic-based clustering for time series data. Data Min. Knowl. Discov., 13(3):335--364, 2006. Google ScholarDigital Library
- W. Zhang, G. Yang, and Z.Wu. Genetic Programming-based Modeling on Chaotic Time Series. In Proc. 3rd Intl Conf. on Machine Learning and Cybernetics, pages 2347--2352. IEEE, 2004.Google ScholarCross Ref
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