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Biogeography-Based Optimization in Machine Learning

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Book cover Biogeography-Based Optimization: Algorithms and Applications

Abstract

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.

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Authors and Affiliations

  1. Hangzhou Institute of Service Engineering, Hangzhou Normal University, Hangzhou, Zhejiang, China

    Yujun Zheng

  2. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China

    Xueqin Lu, Minxia Zhang & Shengyong Chen

Authors
  1. Yujun Zheng
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  2. Xueqin Lu
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  3. Minxia Zhang
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  4. Shengyong Chen
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Corresponding author

Correspondence to Yujun Zheng .

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

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Zheng, Y., Lu, X., Zhang, M., Chen, S. (2019). Biogeography-Based Optimization in Machine Learning. In: Biogeography-Based Optimization: Algorithms and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-13-2586-1_9

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