Научная электронная библиотека ULRICH'S Periodicals Directory

Сибирский государственный университет
науки и технологий имени академика М.Ф. Решетнева

Сибирский аэрокосмический журнал
ISSN 2712-8970 (Print) ISSN 2782-5760 (on-line)

Сведения об образовательной организации

UDK 519.6
HYBRIDIZATION OF LOCAL SEARCH WITH SELF-CONFIGURING GENETIC PROGRAMMING ALGORITHM FOR AUTOMATED FUZZY CLASSIFIER DESIGN
M. E. Semenkina*
Siberian State Aerospace University named after academician M. F. Reshetnev 31, Krasnoyarsky Rabochy Av., Krasnoyarsk, 660014, Russian Federation E-mail: semenkina88@mail.ru
A fuzzy classifier is one of the intelligent information technologies allowing the generation of a fuzzy rule base suitable for interpretation by human experts. For a fuzzy classifier automated design the hybrid self-configuring evolutionary algorithm is proposed. The self-configuring genetic programming algorithm is suggested for the choice of effective fuzzy rule bases. For the tuning of linguistic variables the self-configuring genetic algorithm is used. A hybridization of self-configuring genetic programming algorithms (SelfCGPs) with a local search in the space of trees is fulfilled to improve their performance for fuzzy rule bases automated design. The local search is implemented with two neighborhood systems (1-level and 2-level neighborhoods), three strategies of a tree scanning (“full”, “incomplete” and “truncated”) and two ways of a movement between adjacent trees (transition by the first improvement and the steepest descent). The Lamarckian local search is applied on each generation to ten percent of best individuals. The performance of all developed memetic algorithms is estimated on a representative set of test problems of the functions approximation as well as on real-world classification problems. It is shown that developed memetic algorithm requires comparable amount of computational efforts but outperforms the original SelfCGP for the fuzzy rule bases automated design. The best variant of the local search always uses the steepest descent and full scanning for fuzzy classifier design. Additional advantage of the approach proposed is a possibility of the automated features selection. The numerical experiment results show the competitiveness of the approach proposed.
genetic programming algorithm, self-configuration, fuzzy classifier, local search on discrete structures.
References
  1. Ishibuchi H., Nakashima T., Murata T. Performance Evaluation of Fuzzy Classifier Systems for Multidimensional Pattern Classification Problems. IEEE Trans. on Systems, Man, and Cybernetics, 1999, vol. 29, p. 601–618.
  2. Cordón O., Herrera F., Hoffmann F. and Magdalena L. Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases. Singapore: World Scientific. 2001.
  3. Herrera F. Genetic Fuzzy Systems: Taxonomy, Current Research Trends and Prospects. Evol. Intel. 2008, vol. 1, no. 1, p. 27–46.
  4. Semenkina M. E. [Effectiveness investigation of self-adaptive evolutionary algorithms for data mining information technology design]. Iskusstvennyy intellekt i prinyatiye resheniy. 2013, no. 1, p. 13–23 (In Russ.).
  5. Meyer-Nieberg S., Beyer H.-G. Self-Adaptation in Evolutionary Algorithms. Lobo F. G., Lima C. F., Michalewicz Z. (eds.) Parameter Setting in Evolutionary Algorithm, 2007, vol. 54, p. 47–75.
  6. Gomez J. Self Adaptation of Operator Rates in Evolutionary Algorithms. Deb, K. et al. (eds.) GECCO 2004. LNCS, 2004, vol. 3102, p. 1162–1173.
  7. Semenkin E., Semenkina M. Self-Configuring Genetic Programming Algorithm with Modified Uniform Crossover Operator. Proceedings of the IEEE Congress on Evolutionary Computation (IEEE CEC), 2012, p. 1918–1923.
  8. Semenkin E. S., Semenkina M. E. Self-Configuring Genetic Algorithm with Modified Uniform Crossover Operator. Advances in Swarm Intelligence, Lecture Notes in Computer Science, vol. 7331. Springer-Verlag, Berlin Heidelberg, 2012, p. 414–421.
  9. Finck S. et al. Real-Parameter Black-Box Optimization Benchmarking. Presentation of the noiseless functions. Technical Report Researh Center PPE. 2009.
  10. O’Neill M., Vanneschi L., Gustafson S., Banzhaf W. Open Issues in Genetic Programming. Genetic Programming and Evolvable Machines 11, 2010, p. 339–363.
  11. Poli R., Langdon W.B., McPhee N.F. A Field Guide to Genetic Programming. Published via http://lulu.com. 2008. Available at: http://www.gp-field-guide.org.uk.
  12. Semenkina M., Semenkin E. Hybrid Self-configuring Evolutionary Algorithm for Automated Design of Fuzzy Classifier. Advances in Swarm Intelligence. Lecture Notes in Computer Science, Springer-Verlag, Berlin, Hedelberg, 2014, vol. 8791, part 1, p. 310–317.
  13. 13. Frank A., Asuncion A. UCI Machine Learning Repository (2010) Irvine, CA: University of California, School of Information and Computer Science. Available at: http://archive.ics.uci.edu/ml.
  14. Semenkin E., Semenkina M. Artificial Neural Networks Design with Self-Configuring Genetic Programming Algorithm. Filipic B., Silc J. (Eds.) Bio-inspired Optimization Methods and their Applications: Proceedings of the Fifth International conference BIOMA 2012, 2012, p. 291–300.
  15. Semenkin E. S., Semenkina M. E., Panfilov I. A. Neural Network Ensembles Design with Self-Configuring Genetic Programming Algorithm for Solving Computer Security Problems. Computational Intelligence in Security for Information Systems, Advances in Intelligent Systems and Computing. Springer-Verlag, Berlin Heidelberg, 2012, vol. 189, p. 25–32.
  16. Huang J.-J., Tzeng G.-H., Ong Ch.-Sh. Two-Stage Genetic Programming (2SGP) for the Credit Scoring Model. Applied Mathematics and Computation, 2006, vol. 174, p. 1039–1053.
  17. Sergienko R., Semenkin E., Bukhtoyarov V. Michigan and Pittsburgh Methods Combining for Fuzzy Classifier Generating with Coevolutionary Algorithm for Strategy Adaptation. IEEE Congress on Evolutionary Computation, IEEE Press, New Orleans, LA, 2011.

Semenkina Maria Evgen’evna – Cand. Sc., Docent, Department of the Higher Mathematics, Siberian State Aerospace University named after academician M. F. Reshetnev. Е-mail: semenkina88@mail.ru