Biogeography-Based Optimization in Machine Learning

  • Yujun ZhengEmail author
  • Xueqin Lu
  • Minxia Zhang
  • Shengyong Chen


Artificial neural networks (ANNs) have powerful function approximation and pattern classification capabilities, but their performance is greatly affected by structural design and parameter selection. This chapter introduces how to use BBO and its variants for optimizing structures and parameters of ANNs. The results show that BBO is a powerful method for enhancing the performance of many machine learning models.


  1. 1.
    Blake CL, Merz CJ (1998) UCI repository of machine learning databases.
  2. 2.
    David OE, Greental I (2014) Genetic algorithms for evolving deep neural networks. In: Proceedings of the GECCO, pp 1451–1452.
  3. 3.
    Hinton GE (1989) Connectionist learning procedures. Artif Intell 40(1):185–234. Scholar
  4. 4.
    Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507MathSciNetCrossRefGoogle Scholar
  5. 5.
    Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Nat Acad Sci 79:2554–2558. Scholar
  6. 6.
    Juang CF, Hsiao CM, Hsu CH (2010) Hierarchical cluster-based multispecies particle-swarm optimization for fuzzy-system optimization. IEEE Trans Fuzzy Syst 18:14–26. Scholar
  7. 7.
    Lee H, Grosse R, Ranganath R, Ng AY (2009) Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th annual international conference on machine learning, pp 609–616.
  8. 8.
    Leung FHF, Lam HK, Ling SH, Tam PKS (2003) Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans Neural Netw 14:79–88. Scholar
  9. 9.
    Liang JJ, Qin AK, Suganthan P, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10:281–295. Scholar
  10. 10.
    Lin CT (1996) Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems. Prentice Hall PTR, Upper Saddle RiverGoogle Scholar
  11. 11.
    Ma H, Simon D (2011) Blended biogeography-based optimization for constrained optimization. Eng Appl Artif Int 24:517–525. Scholar
  12. 12.
    McCulloch WS, Pitts WH (1943) A logical calculus for the ideas immanent in nervous activity. Bull Math Biophys 5:115–133. Scholar
  13. 13.
    Negnevitsky M (2005) Artificial intelligence: a guide to intelligent systems. Pearson Education, Essex (2005)Google Scholar
  14. 14.
    ao Paulo Papa J, Scheirer W, Cox DD (2016) Fine-tuning deep belief networks using harmony search. Appl Soft Comput 46:875–885. Scholar
  15. 15.
    Qin AK, Huang VL, Suganthan P (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13:398–417. Scholar
  16. 16.
    Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12:702–713. Scholar
  17. 17.
    Song Q, Zheng YJ, Xue Y, Sheng WG, Zhao MR (2017) An evolutionary deep neural network for predicting morbidity of gastrointestinal infections by food contamination. Neurocomputing 226:16–22. Scholar
  18. 18.
    Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning, pp 1096–1103. ACM, New York, NY, USA.
  19. 19.
    Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–3408MathSciNetzbMATHGoogle Scholar
  20. 20.
    Wu GD, Zhu ZW, Huang PH (2011) A TS-type maximizing-discriminability-based recurrent fuzzy network for classification problems. IEEE Trans Fuzzy Syst 19:339–352. Scholar
  21. 21.
    Zhang JR, Zhang J, Lok TM, Lyu MR (2007) A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training. Appl Math Comput 185:1026–1037. Scholar
  22. 22.
    Zheng YJ, Ling HF, Wu XB, Xue JY (2014) Localized biogeography-based optimization. Soft Comput 18:2323–2334. Scholar
  23. 23.
    Zheng YJ, Ling HF, Xue JY (2014) Ecogeography-based optimization: enhancing biogeography-based optimization with ecogeographic barriers and differentiations. Comput Oper Res 50:115–127. Scholar
  24. 24.
    Zheng YJ, Ling HF, Chen SY, Xue JY (2015) A hybrid neuro-fuzzy network based on differential biogeography-based optimization for online population classification in earthquakes. IEEE Trans Fuzzy Syst 23:1070–1083. Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. and Science Press, Beijing 2019

Authors and Affiliations

  • Yujun Zheng
    • 1
    Email author
  • Xueqin Lu
    • 2
  • Minxia Zhang
    • 2
  • Shengyong Chen
    • 2
  1. 1.Hangzhou Institute of Service EngineeringHangzhou Normal UniversityHangzhouChina
  2. 2.College of Computer Science and TechnologyZhejiang University of TechnologyHangzhouChina

Personalised recommendations