Skip to main content

Diagnosis of Parkinson’s disease using evolutionary algorithms


This paper describes the novel application of an evolutionary algorithm to discriminate Parkinson’s patients from age-matched controls in their response to simple figure-copying tasks. The reliable diagnosis of Parkinson’s disease is notoriously difficult to achieve with misdiagnosis reported to be as high as 25% of cases. The approach described in this paper aims to distinguish between the velocity profiles of pen movements of patients and controls to identify distinguishing artifacts that may be indicative of the Parkinson’s symptom bradykinesia. Results are presented for 12 patients with Parkinson’s disease and 10 age-match controls. An algorithm was evolved using half the patient and age-matched control responses, which was then successfully used to correctly classify the remaining responses. A more rigorous “leave one out” strategy was also applied to the test data with encouraging results.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10


  1. 1.

    Berardelli, A., Rothwell, J.C., Thompson, P.D., Hallett M.: Pathophysiology of bradykinesia in Parkinson’s disease. Brain 124(11):2131–2146 (2001)

    Article  Google Scholar 

  2. 2.

    Elble, R.J., Koller, J.: Tremor. John Hopkins, Baltimore, (1990)

  3. 3.

    Langdon, W.: Quadratic bloat in genetic programming. In: Whitley, D., Goldberg, D., Cantu-Paz, E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), Las Vegas, Nevada, pp. 451–458. Morgan Kaufmann, San Francisco, California, USA (2000)

  4. 4.

    Lones, M.A., Tyrrell, A.M.: Enzyme genetic programming. In: Kim, J.-H., Zhang, B.-T., Fogel, G., Kuscu, I. (eds.) Proceedings of the 2001 Congress on Evolutionary Computation, CEC 2001, vol. 2, pp. 1183–1190. IEEE Press, New Jersy, USA (2001)

  5. 5.

    Lones, M.A., Tyrrell, A.M.: Crossover and bloat in the functionality model of enzyme genetic programming. In Fogel, D.B., El-Sharkawi, M.A., Yao, X., Greenwood, G., Iba, H., Marrow, P., Shackleton, M. (eds.) Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, pp. 986–992. IEEE Press, New Jersy, USA (2002)

  6. 6.

    Lones, M.A., Tyrrell, A.M.: Biomimetic representation with enzyme genetic programming. Gen Program Evolvable Machines 3(2):193–217 (2002)

    MATH  Article  Google Scholar 

  7. 7.

    Lones M.A.: Enzyme Genetic Programming. PhD Thesis, University of York, UK (2003)

  8. 8.

    Lones, M.A., Tyrrell, A.M.: Modelling biological evolvability: implicit context and variation filtering in enzyme generic programming. BioSystems 76(2), 229–238 (2004)

    Article  Google Scholar 

  9. 9.

    Miller, J.F., Thomson, P.: Cartesian Genetic Programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J.F., Nordin, P., Fogarty, T.C. (eds.) Third European Conference on Genetic Programming, Proceedings of EuroGP’2000, Edinburgh, vol. 1802, pp. 121–132. Springer-Verlag, Berlin (2000)

  10. 10.

    Miller, J.F., Job, D., Vasilev, V.K.: Principles in the evolutionary design of digital circuits—Part I. Genetic Programming and Evolvable Machines 1:7–36 (2000)

    MATH  Article  Google Scholar 

  11. 11.

    Playfer, J.R.: Parkinson’s disease: classic diseases revisited. Postgrad Med J 73:257–64 (1997)

    Article  Google Scholar 

  12. 12.

    Smith, S.L., Leggett, S., Tyrrell, A.M.: An implicit context representation for evolving image processing filters. In: Proceedings of the 7th Workshop on Evolutionary Computation in Image Analysis and Signal Processing, Lecture Notes in Computer Science, vol. 3449, pp. 407–416 (2005)

Download references


We would like to thanks the staff of Broadgreen Hospital, Liverpool, UK for their help in acquiring the patient data for this work.

Author information



Corresponding author

Correspondence to Stephen L. Smith.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Smith, S.L., Gaughan, P., Halliday, D.M. et al. Diagnosis of Parkinson’s disease using evolutionary algorithms. Genet Program Evolvable Mach 8, 433–447 (2007).

Download citation


  • Parkinson’s disease
  • Evolutionary algorithms
  • Cartesian genetic programing