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Neural Computing & Applications

, Volume 6, Issue 4, pp 214–228 | Cite as

Genetic programming for prediction and control

  • D. C. Dracopoulos
  • S. Kent
Articles

Abstract

The relatively ‘new’ field of genetic programming has received a lot of attention during the last few years. This is because of its potential for generating functions which are able to solve specific problems. This paper begins with an extensive overview of the field, highlighting its power and limitations and providing practical tips and techniques for the successful application of genetic programming in general domains. Following this, emphasis is placed on the application of genetic programming to prediction and control. These two domains are of extreme importance in many disciplines. Results are presented for an oral cancer prediction task and a satellite attitude control problem. Finally, the paper discusses how the convergence of genetic programming can be significantly speeded up through bulk synchronous model parallelisation.

Keywords

Evolutionary computing Evolutionary control Genetic programming Parallel computing Prediction 

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References

  1. 1.
    Darwin C. On the Origin of Species. John Murray, London, 1859Google Scholar
  2. 2.
    Koza JR. Genetic Programming: on the Programming of Computers by means of Natural Selection. MIT Press, Cambridge, MA, 1992Google Scholar
  3. 3.
    Koza JR. Hierarchical genetic algorithms operating on populations of computer programs. In: NS Sridharan (ed). Proc 11th International Joint Conference on Artificial Intelligence IJCAI-89, San Mateo, CA, 1989; 1: 768–774Google Scholar
  4. 4.
    Xiao J, Michalewicz Z, Zhang L, Krzysztof T. Adaptive evolutionary planner/navigator for mobile robots. TEC 1997; 1(1): 18–28Google Scholar
  5. 5.
    Montana DJ. Strongly typed genetic programming. BBN Technical Report #7866, Bolt Beranek and Newman, Inc., 10 Moulton Street, Cambridge, MA 02138, USA, March 1994Google Scholar
  6. 6.
    Dracopoulos DC. Evolutionary learning algorithms for neural adaptive control. Springer-Verlag, London, August 1997Google Scholar
  7. 7.
    Sutton RS, Barto AG. Reinforcement Learning: An Introduction. MIT Press/Bradford Books, 1998Google Scholar
  8. 8.
    Goldberg DE. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA, 1989Google Scholar
  9. 9.
    Blickle T, and Thiele L. A comparison of selection schemes used in genetic algorithms. Technical Report 11, Computer Engineering and Communications Networks Lab (TIK), Swiss Federal Institute of Technology (ETH), Gloriastrasse 3, 8092 Zurich, Switzerland, December 1995Google Scholar
  10. 10.
    Syswerd G. Uniform crossover in genetic algorithm. In: JD Schaffer (ed). Proc 3rd International Conference on Genetic Algorithms, 1989Google Scholar
  11. 11.
    Whitley D. The genitor algorithm and selection pressure: why rank based allocation of reproductive trials is best. In: J D Schaffer (ed). Proc 3rd International Conference on Genetic Algorithms, 1989Google Scholar
  12. 12.
    Koza JR. Genetic Programming II. MIT Press, Cambridge, MA, 1994Google Scholar
  13. 13.
    Koza JR. Future work and practical applications of genetic programming. In: T Baeck, DB Fogel, Z Michalewicz (eds). Handbook of Evolutionary Computation, Oxford University Press, UK, 1997; H1.1–1-6Google Scholar
  14. 14.
    Handley SG. The prediction of the degree of exposure to solvent of amino acid residues via genetic programming. Second International Conference on Intelligent Systems for Molecular Biology, Stanford, CA, 1994Google Scholar
  15. 15.
    Handley SG. Classifying nucleic acid sub-sequences as introns or exons using genetic programming. In: C Rawlins, D Clark, R Altman, L Hunter, T Lengauer, S Wodak (eds). Proc 3rd International Conference on Intelligent Systems for Molecular Biology (ISMB-95), Cambridge, UK, 1995; 162–169Google Scholar
  16. 16.
    Koza JR, Andre D. Classifying protein segments as transmembrane domains using architecture-altering operations in genetic programming. In: PJ Angeline, KE Kinnear, Jr. (eds). Advances in Genetic Programming 2, MIT Press, Cambridge, MA, 1996; 155–176Google Scholar
  17. 17.
    Koza JR, Bennett III FH, Andre D, Keane MA. Automated WYWIWYG design of both the topology and component values of electrical circuits using genetic programming. In: JR Koza, DE Goldberg, DB Fogel, RL Riolo (eds). Genetic Programming 1996: Proc 1st Annual Conference, Stanford University, CA, 1996; 123–131Google Scholar
  18. 18.
    Quarles T, Newton AR, Pederson DO, Sangiovanni-Vincentelli A. SPICE 3 Version 3F5 User's Manual. Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA, March 1994Google Scholar
  19. 19.
    Higuchi T, Iwata M, Weixin L (eds). Evolvable Systems: From Biology to Hardware. Springer-Verlag, Berlin, 1997Google Scholar
  20. 20.
    Thompson A. Silicon evolution. In: JR Koza, DE Goldberg, DB Fogel, RL Riolo (eds). Genetic Programming 1996: Proc 1st Annual Conference, Stanford University, CA, 1996; 444–452Google Scholar
  21. 21.
    Koza JR, Bennett III FH, Hutchings JL, Bade SL, Keane MA, Andre D. Rapidly reconfigurable field-programmable gate arrays for accelerating fitness evaluation in genetic programming. In: JR Koza (ed). Late Breaking Papers at the 1997 Genetic Programming Conference, Stanford University, CA, 1997; 121–131Google Scholar
  22. 22.
    Liu W, Murakawa M, Higuchi T. Evolvable hardware for on-line adaptive traffic control in ATM networks. In: JR Koza, K Deb, M Dorigo, DB Fogel, M Garzon, H Iba, RL Riolo (eds). Genetic Programming 1997: Proc 2nd Annual Conference, Stanford University, CA, 1997; 504–509Google Scholar
  23. 23.
    Jullien JA, Downer MC, Zakzrewska J, Speight PM. Evaluation of a screening test for the early detection of oral cancer and pre-cancer. Communications of Dental Health 1995; 12(3)Google Scholar
  24. 24.
    Elliot C. The use of inductive logic programming and data mining techniques to identify people at risk of oral cancer and pre-cancer. Master's thesis, Brunel University, 1996Google Scholar
  25. 25.
    Dracopoulos DC. Genetic algorithms and genetic programming for control. In: D Dasgupta, Z Michalewicz (eds). Evolutionary Algorithms in Engineering Applications. Springer-Verlag, Berlin, 1997; 329–344Google Scholar
  26. 26.
    White DA, Sofge DA. (eds). Handbook of Intelligent Control. Van Nostrand Reinhold, 1992Google Scholar
  27. 27.
    Dracopoulos DC. Evolutionary control of a satellite. In: JR Koza, D Kalyanmoy, M Dorigo, DB Fogel, M Garzon, H Iba, RL Riolo (eds). Genetic Programming 1997: Proceedings of the Second Annual Conference, Stanford, San Francisco, CA, July 13–16 1997Google Scholar
  28. 28.
    Goldstein H. Classical Mechanics, 2nd ed. Addison-Wesley, Reading, MA, 1980Google Scholar
  29. 29.
    Meyer G. On the use of Euler's theorem on rotations for the synthesis of attitude control systems. Technical Report TN D-3643, NASA, 1966Google Scholar
  30. 30.
    Leipnik RB, Newton TA. Double strange attractors in rigid body motion with linear feedback control. Physics Letters 1981; 86A: 63–67Google Scholar
  31. 31.
    Piper GE, Kwatny HG. Complicated dynamics in spacecraft attitude control systems. Journal of Guidance, Control and Dynamics 1992; 15(4): 825–831Google Scholar
  32. 32.
    Bennett III FH, Koza, JR, Andre D, Keane MA. Evolution of a 60 decibel op amp using genetic programming. Proc Int Conference on Evolvable Systems: From Biology to Hardware (ICES-96): Lecture Notes in Computer Science. Springer-Verlag, Berlin, 1996Google Scholar
  33. 33.
    Andre D, Koza JR. Parallel genetic programming: A scalable implementation using the transputer network architecture. In: PJ Angeline, KE Kinnear Jr. (eds). Advances in Genetic Programming 2. MIT Press, Cambridge, MA, 1996; 317–338Google Scholar
  34. 34.
    Juille H, and Pollack JB. Parallel genetic programming and fine-grained SIMD architecture. In: ES Siegel, JR Koza (eds). Working Notes for the AAAI Symposium on Genetic Programming, MIT, Cambridge, MA, 10–12 November 1995; 31–37Google Scholar
  35. 35.
    Valiant LG. A bridging model for parallel computation. Communications of the ACM 1990; 33(8): 103–111Google Scholar
  36. 36.
    Miller R, Reed J. The Oxford BSP Library users' guide. Technical report, University of Oxford, 1993Google Scholar
  37. 37.
    Gordon VS, Whitley D. Serial and parallel genetic algorithms as function optimizers. In: S Forrest (ed). Proc 5th International Conference on Genetic Algorithms, San Francisco, CA, 1993Google Scholar
  38. 38.
    Koza JR, Andre D. Parallel genetic programming on a network of transputers. Technical Report CS-TR-95-1542, Stanford University, Department of Computer Science, January 1995Google Scholar
  39. 39.
    Harris C, Buxton B. GP-COM: A distributed, component-based genetic programming system in C++. Research Note RN/96/2, UCL, Gower Street, London, WC1E 6BT, UK, January 1996Google Scholar
  40. 40.
    Dracopoulos DC, Self D. Parallel genetic programming. Proc UK Parallel 96. Springer-Verlag, Berlin, 1996Google Scholar

Copyright information

© Springer-Verlag London Limited 1997

Authors and Affiliations

  • D. C. Dracopoulos
    • 1
  • S. Kent
    • 1
  1. 1.Department of Information Systems and ComputingBrunel UniversityUxbridgeUK

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