Local Search Based Enhanced Multi-objective Genetic Algorithm of Training Backpropagation Neural Network for Breast Cancer Diagnosis

Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 5)


Recently, several evolutionary algorithms have been proposed on the basis of preference in literature. Most of multi-objective evolutionary algorithms used NSGA-II due to a good performance in comparison with other multi-objective evolutionary algorithms. Our research is focused on enhancement of a well-known evolutionary algorithm NSGA-II by combining a local search method for solving Breast cancer classification problem based on Backpropagation neural network. The use of local search within the enhanced NSGA II operating can accelerate the convergence speed towards the non-dominated front and ensures the solutions attained are well spread over it. The proposed hybrid method has been experimentally evaluated by applying to the Breast cancer classification problem. It has been experimentally shown that the combination of the local search method has a positive impact to the final solution and thus increased the classification accuracy of the results.


Artificial neural networks Local search Backpropagation Non-dominated sorting genetic algorithm 


  1. 1.
    Khosrowshahi, F.: Innovation in artificial neural network learning: learn-on-demand methodology. Autom. Constr. 20(8), 1204–1210 (2011)CrossRefGoogle Scholar
  2. 2.
    Kuo, R., Lin, L.: Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering. Decis. Support Syst. 49(4), 451–462 (2010)CrossRefGoogle Scholar
  3. 3.
    Cheok, C.Y., et al.: Optimization of total phenolic content extracted from Garcinia mangostana Linn. hull using response surface methodology versus artificial neural network. Ind. Crops Prod. 40, 247–253 (2012)CrossRefGoogle Scholar
  4. 4.
    Qasem, S.N., Shamsuddin, S.M.: Memetic elitist pareto differential evolution algorithm based radial basis function networks for classification problems. Appl. Soft Comput. 11(8), 5565–5581 (2011)CrossRefGoogle Scholar
  5. 5.
    Yao, X.: Evolving artificial neural networks. Proc. IEEE 87(9), 1423–1447 (1999)CrossRefGoogle Scholar
  6. 6.
    Pettersson, F., Chakraborti, N., Saxén, H.: A genetic algorithms based multi-objective neural net applied to noisy blast furnace data. Appl. Soft Comput. 7(1), 387–397 (2007)CrossRefGoogle Scholar
  7. 7.
    Delgado, M., Cuellar, M.P., Pegalajar, M.C.: Multiobjective hybrid optimization and training of recurrent neural networks. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 38(2), 381–403 (2008)CrossRefGoogle Scholar
  8. 8.
    Jin, Y., Sendhoff, B., Körner, E.: Evolutionary multi-objective optimization for simultaneous generation of signal-type and symbol-type representations. In: International Conference on Evolutionary Multi-Criterion Optimization. Springer (2005)Google Scholar
  9. 9.
    Liu, G., Kadirkamanathan, V.: Multiobjective criteria for neural network structure selection and identification of nonlinear systems using genetic algorithms. IEE Proc. Control Theor. Appl. 146(5), 373–382 (1999)CrossRefGoogle Scholar
  10. 10.
    Abbass, H.A., Sarker, R.: Simultaneous evolution of architectures and connection weights in ANNs. In: Proceedings of Artificial Neural Networks and Expert System Conference (2001)Google Scholar
  11. 11.
    Ibrahim, A.O., Hasan, S., Noman, S.: Memetic Elitist Pareto evolutionary algorithm of three-term backpropagation network for classification problems. Int. J. Adv. Soft Comput. Appl. 6(3), 1 (2014)Google Scholar
  12. 12.
    Ibrahim, A.O., et al.: Hybrid NSGA-II of three-term backpropagation network for multiclass classification problems. In: 2014 International Conference on Computer and Information Sciences (ICCOINS). IEEE (2014)Google Scholar
  13. 13.
    Bonissone, P.P., et al.: Hybrid soft computing systems: industrial and commercial applications. Proc. IEEE 87(9), 1641–1667 (1999)CrossRefGoogle Scholar
  14. 14.
    Seera, M., Lim, C.P.: A hybrid intelligent system for medical data classification. Expert Syst. Appl. 41(5), 2239–2249 (2014)CrossRefGoogle Scholar
  15. 15.
    Deja, R., et al.: Hybrid approach to the generation of medical guidelines for insulin therapy for children. Inf. Sci. 384, 157–173 (2017)CrossRefGoogle Scholar
  16. 16.
    Fan, C.-Y., et al.: A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification. Appl. Soft Comput. 11(1), 632–644 (2011)CrossRefGoogle Scholar
  17. 17.
    Gorzałczany, M.B., Rudziński, F.: Interpretable and accurate medical data classification–a multi-objective genetic-fuzzy optimization approach. Expert Syst. Appl. 71, 26–39 (2017)CrossRefGoogle Scholar
  18. 18.
    Zheng, B., Yoon, S.W., Lam, S.S.: Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Syst. Appl. 41(4), 1476–1482 (2014)CrossRefGoogle Scholar
  19. 19.
    Turabieh, H.: GA-based feature selection with ANFIS approach to breast cancer recurrence. Int. J. Comput. Sci. Issues (IJCSI) 13(1), 36 (2016)CrossRefGoogle Scholar
  20. 20.
    Ahmad, F., et al.: A GA-based feature selection and parameter optimization of an ANN in diagnosing breast cancer. Pattern Anal. Appl. 18(4), 861–870 (2015)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Ibrahim, A.O., et al.: Intelligent multi-objective classifier for breast cancer diagnosis based on multilayer perceptron neural network and differential evolution. In: 2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE). IEEE (2015)Google Scholar
  22. 22.
    Deb, K., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  23. 23.
    De Jong, K.A., Spears, W.M.: A formal analysis of the role of multi-point crossover in genetic algorithms. Ann. Math. Artif. Intell. 5(1), 1–26 (1992)CrossRefzbMATHGoogle Scholar
  24. 24.
    Črepinšek, M., Liu, S.-H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. (CSUR) 45(3), 35 (2013)zbMATHGoogle Scholar
  25. 25.
    Qasem, S.N., et al.: Memetic multiobjective particle swarm optimization-based radial basis function network for classification problems. Inf. Sci. 239, 165–190 (2013)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Abbass, H.A.: An evolutionary artificial neural networks approach for breast cancer diagnosis. Artif. Intell. Med. 25(3), 265–281 (2002)CrossRefGoogle Scholar
  27. 27.
    Ibrahim, A.O., et al.: Three-term backpropagation network based on elitist multiobjective genetic algorithm for medical diseases diagnosis classification. Life Sci. J. 10(4), 1815–1822 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of Computer and TechnologyAlzaiem Alazhari UniversityKhartoumSudan
  2. 2.Arab Open UniversityKhartoumSudan
  3. 3.UTM Big Data CentreUniversiti Teknologi MalaysiaSkudaiMalaysia
  4. 4.FSKPM FacultyUniversity Malaysia Sarawak (UNIMAS)Kota SamarahanMalaysia

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