FIFTHTM: A Stack Based GP Language for Vector Processing

  • Kenneth Holladay
  • Kay Robbins
  • Jeffery von Ronne
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4445)


FIFTHTM, a new stack-based genetic programming language, efficiently expresses solutions to a large class of feature recognition problems. This problem class includes mining time-series data, classification of multivariate data, image segmentation, and digital signal processing (DSP). FIFTH is based on FORTH principles. Key features of FIFTH are a single data stack for all data types and support for vectors and matrices as single stack elements. We demonstrate that the language characteristics allow simple and elegant representation of signal processing algorithms while maintaining the rules necessary to automatically evolve stack correct and control flow correct programs. FIFTH supports all essential program architecture constructs such as automatically defined functions, loops, branches, and variable storage. An XML configuration file provides easy selection from a rich set of operators, including domain specific functions such as the Fourier transform (FFT). The fully-distributed FIFTH environment (GPE5) uses CORBA for its underlying process communication.


Genetic Programming vectors linear GP GP environment 


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  1. 1.
    Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D. (eds.): Genetic Programming-An Introduction; On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, San Francisco (1998)zbMATHGoogle Scholar
  2. 2.
    Spector, L., Klein, J., Keijzer, M.: The Push3 Execution Stack and the Evolution of Control. In: GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, vol. 2, pp. 1689–1696. ACM Press, New York (2005)CrossRefGoogle Scholar
  3. 3.
    Spector, L., Perry, C., Klein, J., Keijzer, M.: Hampshire College School of Cognitive Science, Push 3.0 Programming Language Description., (Accessed September 2003)
  4. 4.
    Gagn, C., Parizeau, M.: Open BEAGLE: A New C++ Evolutionary Computation Framework. In: Genetic and Evolutionary Computation Conference. GECCO 2002, New York, Morgan Kaufmann, San Francisco (2002)Google Scholar
  5. 5.
    Perkis, T.: Stack-Based Genetic Programming. In: Proceedings of the 1994 IEEE World Congress on Computational Intelligence, Orlando, Florida, USA, IEEE, Los Alamitos (1994)Google Scholar
  6. 6.
    Spector, L., Robinson, A.: Genetic Programming and Autoconstructive Evolution with the Push Programming Language. In: Genetic Programming and Evolvable Machines (2002)Google Scholar
  7. 7.
    Tchernev, E.: Stack-Correct Crossover Methods in Genetic Programming. In: Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002), New York. AAAI, Menlo Park (2002)Google Scholar
  8. 8.
    Silva, S.: GPLAB - A Genetic Programming Toolbox for MATLAB (2005)Google Scholar
  9. 9.
    DiscipulusTM Genetic-Programming Software. RML Technologies, Inc. (2004) Google Scholar
  10. 10.
    Teller, A., Veloso, M.: Program Evolution for Data Mining. The International Journal of Expert Systems 8, 216–236 (1995)Google Scholar
  11. 11.
    Sharman, K.C., Esparcia-Alcazar, A.I., Li, Y.: Evolving Digital Signal Processing Algorithms by Genetic Programming. Faculty of Engineering, Glasgow, Scotland (1995)Google Scholar
  12. 12.
    Rizki, M.M., Tamburino, L.A.: Evolutionary Computing Applied To Pattern Recognition. In: Koza, J.R., et al. (eds.) Proceedings of the Third Annual Conference on Genetic Programming, University of Wisconsin, Madison, Wisconsin, USA, pp. 777–785. Morgan Kaufmann, San Francisco (1998)Google Scholar
  13. 13.
    Rather, E., Bradley, M.: Programming Languages - FORTH (X3J14 dpANS-6). American National Standards Institute, Inc. (1993)Google Scholar
  14. 14.
    Mammone, R.J., Rothaker, R.J., Podilchuk, C.I.: Estimation of carrier frequency, modulation type, and bit rate of an unknown modulated signal. IEEE International Conference on Communications 2, 1006–1012 (1987)Google Scholar
  15. 15.
    Cardelli, L.: Type Systems. In: Tucker, A.B. (ed.) CRC Handbook of Computer Science and Engineering, CRC Press, Boca Raton (2004)Google Scholar
  16. 16.
    Haynes, T.D., Schoenefeld, D.A., Wainwright, R.L.: Type Inheritance in Strongly Typed Genetic Programming. In: Angeline, P.J., Kinnear Jr., K.E. (eds.) Advances in Genetic Programming, 2nd edn., pp. 359–376. MIT Press, Cambridge (1996)Google Scholar
  17. 17.
    MathWorks, The Mathworks, Inc., MAT-File Format (2005),
  18. 18.
    Schmidt, D.C.: Real-time CORBA with TAO (2006),
  19. 19.
    Tchernev, E.B., Phatak, D.S.: Control structures in linear and stack-based Genetic Programming. In: Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference, Seattle, Washington, USA (2004)Google Scholar
  20. 20.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  21. 21.
    Holladay, K., Robbins, K.: A Framework for Automatic Large-Scale Testing and Characterization of Signal Processing Algorithms. In: Military Communications (MILCOM), Monterey, CA (2004)Google Scholar
  22. 22.
    Holladay, K., Robbins, K.: Experimental Analysis of Wavelet Transforms for Estimating PSK Symbol Rate. In: IASTED International Conference on Communication Systems and Applications, Banff, Canada (2004)Google Scholar
  23. 23.
    Kerkut, G.A.: Implications of Evolution. Pergamon Press Inc., New York (1960)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Kenneth Holladay
    • 1
  • Kay Robbins
    • 2
  • Jeffery von Ronne
    • 2
  1. 1.Southwest Research Institute, San Antonio, Texas 
  2. 2.University of Texas at San Antonio, San Antonio, Texas 

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