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Using Optimization Techniques in Grammatical Evolution

Author

Listed:
  • Ioannis G. Tsoulos

    (Department of Informatics and Telecommunications, University of Ioannina, 47150 Kostaki Artas, Greece)

  • Alexandros Tzallas

    (Department of Informatics and Telecommunications, University of Ioannina, 47150 Kostaki Artas, Greece)

  • Evangelos Karvounis

    (Department of Informatics and Telecommunications, University of Ioannina, 47150 Kostaki Artas, Greece)

Abstract

The Grammatical Evolution technique has been successfully applied to a wide range of problems in various scientific fields. However, in many cases, techniques that make use of Grammatical Evolution become trapped in local minima of the objective problem and fail to reach the optimal solution. One simple method to tackle such situations is the usage of hybrid techniques, where local minimization algorithms are used in conjunction with the main algorithm. However, Grammatical Evolution is an integer optimization problem and, as a consequence, techniques should be formulated that are applicable to it as well. In the current work, a modified version of the Simulated Annealing algorithm is used as a local optimization procedure in Grammatical Evolution. This approach was tested on the Constructed Neural Networks and a remarkable improvement of the experimental results was shown, both in classification data and in data fitting cases.

Suggested Citation

  • Ioannis G. Tsoulos & Alexandros Tzallas & Evangelos Karvounis, 2024. "Using Optimization Techniques in Grammatical Evolution," Future Internet, MDPI, vol. 16(5), pages 1-20, May.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:5:p:172-:d:1395999
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    References listed on IDEAS

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    4. Noorian, Farzad & de Silva, Anthony M. & Leong, Philip H. W., 2016. "gramEvol: Grammatical Evolution in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i01).
    Full references (including those not matched with items on IDEAS)

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