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23rd International Conference on Soft Computing

MENDEL 2017: Recent Advances in Soft Computing pp 192–207Cite as

Grammatical Evolution for Classification into Multiple Classes

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 837))

Abstract

In this contribution the authors deal with classification problems using an approach based on grammatical evolution. The named method is used to create short executable structures which are evolved to classify given input into multiple classes. Resulting structures are usable as computer programs for embedded devices with low computational resources. An universal formula for fitness value calculation of the evolved individual is introduced and an example of planar graphical objects classification in generated image dataset is presented. The presented approach is still applicable for general multi-class classification problems. The results of the proposed method are discussed and examined.

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References

  1. Bay, H., Ess, A., Tuytelaars, T.: Speeded-up robust features SURF. Comput. Vis. Image Underst. 110(3), 346–359 (2008). ISSN 1077-3142

    Article  Google Scholar 

  2. Boháč, M., Lýsek, J., Motyčka, A., Cepl, M.: Face recognition by means of new algorithms. In: 17th International Conference on Soft Computing, MENDEL 2011, pp. 324–329. Brno University of Technology, Czech Republic (2011). ISBN 978-80-214-4302-0

    Google Scholar 

  3. Burke, E.K., Gustafson, S., Kendall, G.: Diversity in genetic programming: an analysis of measures and correlation with fitness. IEEE Trans. Evol. Comput. 8(1), 47–62 (2004). ISSN 1089-778X

    Article  Google Scholar 

  4. Čížek, L., Šťastný, J.: Comparison of genetic algorithm and graph-based algorithm for the TSP. In: 19th International Conference on Soft Computing, MENDEL 2013, pp. 433–438. Brno University of Technology, Czech Republic (2013). ISSN 1803-381, ISBN 978-80-214-4755-4

    Google Scholar 

  5. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press (1992). ISBN 0-262-11170-5

    Google Scholar 

  6. Lowe, G.D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004). ISSN 0920-5691

    Article  Google Scholar 

  7. Liu, J., Tang, Y.Y., Cao, Y.C.: An evolutionary autonomous agents approach to image feature extraction. IEEE Trans. Evol. Comput. 1(2), 141–158 (1997). ISSN 1089–778X

    Article  Google Scholar 

  8. Lýsek, J., Šťastný, J., Motyčka, A.: Object recognition by means of evolved detector and classifier program. In: 18th International Conference of Soft Computing, MENDEL 2012, pp. 82–87. Brno University of Technology, Czech Republic (2012). ISSN 1803-3814, ISBN 978-80-214-4540-6

    Google Scholar 

  9. Lýsek, J., Šťastný, J.: Classification of economic data into multiple classes by means of evolutionary methods. In: Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, vol. 61, no. 7, pp. 2445–2449 (2013). ISSN 1211-8516

    Google Scholar 

  10. Marić, M.: An efficient genetic algorithm for solving the multi-level uncapacitated facility location problem. Comput. Inform. 29(2), 183–201 (2010). ISSN 1335-9150

    MathSciNet  MATH  Google Scholar 

  11. Munk, M., Kapusta, J., Švec, P., Turčáni, M.: Data advance preparation factors affecting results of sequence rule analysis in web log mining. E & M Ekonomie a Manag. 13(4), 143–160 (2010). ISSN 1212–3609

    Google Scholar 

  12. Omran, M.G.H., Engelbrecht, A.P., Salman, A.: Differential evolution methods for unsupervised image classification. In: The 2005 IEEE Congress on Evolutionary Computation, Evolutionary Computation, vol. 2, pp. 966–973 (2005). ISBN 0-7803-9363-5

    Google Scholar 

  13. Perez, B.C., Olague, G.: Evolutionary learning of local descriptor operators for object. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, GECCO 2009, USA, pp. 1051–1058 (2009). ISBN 978-1-60558-325-9

    Google Scholar 

  14. Škorpil, V., Šťastný, J.: Comparison methods for object recognition. In: Proceedings of the 13th WSEAS International Conference on Systems, Rhodos, Greece, pp. 607–610 (2009). ISBN 978-960-474-097- 0

    Google Scholar 

  15. Stanimirović, Z.: A genetic algorithm approach for the capacitated single allocation P-Hub median problem. Comput. Inform. 29(1), 117–132 (2010). ISSN 1335-9150

    MATH  Google Scholar 

  16. Šťastný, J., Škorpil, V.: Analysis of algorithms for radial basis function neural network. In: Personal Wireless Communications, no. 1, pp. 54–62. Springer (2007). ISSN 1571-5736, ISBN 978-0-387-74158-1

    Google Scholar 

  17. Tackett, W.A.: Genetic programming for feature discovery and image discrimination. In: Proceedings of the 5th International Conference on Genetic Algorithms, pp. 303–311. Morgan Kaufmann Publishers Inc., San Francisco (1993). ISBN 1-55860-299-2

    Google Scholar 

  18. Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recogn. 37(1), 1–19 (2004). ISSN 1054-6618

    Article  Google Scholar 

  19. Zhang, M., Ciesielski, V.B., Andreae, P.: A domain-independent window approach to multiclass object detection using genetic programming. EURASIP J. Appl. Sig. Process. 2003, 841–859 (2003). ISSN 1687-6180

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Informatics, Mendel University in Brno, Zemědělská 1, 613 00, Brno, Czech Republic

    Jiří Lýsek & Jiří Šťastný

  2. Institute of Automation and Computer Science, Brno University of Technology, Technická 2, 616 00, Brno, Czech Republic

    Jiří Šťastný

Authors
  1. Jiří Lýsek
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  2. Jiří Šťastný
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Corresponding author

Correspondence to Jiří Lýsek .

Editor information

Editors and Affiliations

  1. Department of Applied Computer Science, Institute of Automation and Computer Science, Faculty of Mechanical Engineering, Brno University of Technology, Brno, Czech Republic

    Radek Matoušek

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Lýsek, J., Šťastný, J. (2019). Grammatical Evolution for Classification into Multiple Classes. In: Matoušek, R. (eds) Recent Advances in Soft Computing . MENDEL 2017. Advances in Intelligent Systems and Computing, vol 837. Springer, Cham. https://doi.org/10.1007/978-3-319-97888-8_17

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