ANN-based Classifiers Automatically Generated by New Multi-objective Bionic Algorithm

Shakhnaz Akhmedova, Eugene Semenkin

2015

Abstract

An artificial neural network (ANN) based classifier design using the modification of a meta-heuristic called Co-Operation of Biology Related Algorithms (COBRA) for solving multi-objective unconstrained problems with binary variables is presented. This modification is used for the ANN structure selection. The weight coefficients of the ANN are adjusted with the original version of COBRA. Two medical diagnostic problems, namely Breast Cancer Wisconsin and Pima Indian Diabetes, were solved with this technique. Experiments showed that both variants of COBRA demonstrate high performance and reliability in spite of the complexity of the optimization problems solved. ANN-based classifiers developed in this way outperform many alternative methods on the mentioned classification problems. The workability of the proposed meta-heuristic optimization algorithms was confirmed.

References

  1. Akhmedova, Sh., Semenkin, E., 2013(1). Co-Operation of Biology-Related Algorithms. In IEEE Congress on Evolutionary Computations. IEEE Publications.
  2. Akhmedova, Sh., Semenkin, E., 2013(2). New optimization metaheuristic based on co-operation of biology related algorithms, Vestnik. Bulletine of Siberian State Aerospace University. Vol. 4 (50).
  3. Akhmedova, Sh., Semenkin E., 2014. Co-Operation of Biology Related Algorithms Meta-Heuristic in ANNBased Classifiers Design. In IEEE World Congress on Computational Intelligence. IEEE Publications.
  4. Fonseca, C.M., Fleming, P.J., 1993. Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. In The Fifth International Conference on Genetic Algorithms.
  5. Frank, A., Asuncion. A., 2010. UCI Machine Learning Repository. Irvine, University of California, School of Information and Computer Science. http://archive.ics.uci.edu/ml.
  6. Jordan, M.I., Jacobs, R.A., 1994. Hierarchical Mixture of Experts and the EM Algorithm. Neural Computation, 6.
  7. Kennedy, J., Eberhart, R., 1995. Particle Swarm Optimization. In IEEE International Conference on Neural Networks.
  8. Kennedy, J., Eberhart, R., 1997. A discrete binary version of the particle swarm algorithm. In World Multiconference on Systemics, Cybernetics and Informatics.
  9. Kursawe, F., 1990. A variant of evolution strategies for vector optimization. Parallel Problem Solving for Nature, H.P. Schwefel, R. Männer, Ed. Lecture Notes Computer Science, Springer-Verlag, Berlin, Vol. 496.
  10. Liang, J.J., Qu, B.Y., Suganthan, P.N., Hernandez-Diaz, A.G., 2012. Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session on RealParameter Optimization. Technical Report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China, and Technical Report, Nanyang Technological University, Singapore.
  11. Marcano-Cedeno, A., Quintanilla-Domínguez, J., Andina, D., 2011. WBCD breast cancer database classification applying artificial metaplasticity neural network. Expert Systems with Applications: An International Journal, vol. 38, issue 8.
  12. Mostaghim, S., Teich, J., 2003. Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). In IEEE Swarm Intelligence Symposium. IEEE Service Center.
  13. Sasaki, T., Tokoro, M., 1999. Evolving Learnable Neural Networks under Changing Environments with Various Rates of Inheritance of Acquired Characters: Comparison between Darwinian and Lamarckian Evolution. Artificial Life 5 (3).
  14. Schaffer, J.D., 1985. Multiple objective optimization with vector evaluated genetic algorithms. In The 1st International Conference on Genetic Algorithms.
  15. Temurtas, H., Yumusak, N., Temurtas, F., 2009. A comparative study on diabetes disease diagnosis using neural networks. Expert Systems with Applications, vol. 36, no. 4.
  16. Yang, Ch., Tu, X., Chen, J., 2007. Algorithm of Marriage in Honey Bees Optimization Based on the Wolf Pack Search. In International Conference on Intelligent Pervasive Computing.
  17. Yang, X.S., 2009 Firefly algorithms for multimodal optimization. In The 5th Symposium on Stochastic Algorithms, Foundations and Applications.
  18. Yang, X.S., 2010. A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization, Studies in Computational Intelligence. Vol. 284.
  19. Yang, X.S., 2012. Bat Algorithm for Multiobjective Optimization. Bio-Inspired Computation, Vol. 3, no. 5.
  20. Yang, X.S., 2013. Multi-objective firefly algorithm for continuous optimization. Engineering with Computers, Vol. 29, issue 2.
  21. Yang, X.S., Deb, S., 2009. Cuckoo Search via Levy flights. In World Congress on Nature & Biologically Inspired Computing. IEEE Publications.
  22. Yang, X.S., Deb, S., 2011. Multi-objective cuckoo search for design optimization. Computers & Operations Research, Vol. 40.
  23. Zitzler, E., Deb, K., Thiele, L., 2000. Comparison of multiobjective evolutionary algorithms: empirical results. Evolutionary Computation, Vol. 8 (2).
Download


Paper Citation


in Harvard Style

Akhmedova S. and Semenkin E. (2015). ANN-based Classifiers Automatically Generated by New Multi-objective Bionic Algorithm . In Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-122-9, pages 310-317. DOI: 10.5220/0005571603100317


in Bibtex Style

@conference{icinco15,
author={Shakhnaz Akhmedova and Eugene Semenkin},
title={ANN-based Classifiers Automatically Generated by New Multi-objective Bionic Algorithm},
booktitle={Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2015},
pages={310-317},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005571603100317},
isbn={978-989-758-122-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - ANN-based Classifiers Automatically Generated by New Multi-objective Bionic Algorithm
SN - 978-989-758-122-9
AU - Akhmedova S.
AU - Semenkin E.
PY - 2015
SP - 310
EP - 317
DO - 10.5220/0005571603100317