Online citations, reference lists, and bibliographies.

Biogeography-Based Optimization In Machine Learning

Yujun Zheng, Xueqin Lu, Minxia Zhang, Shengyong Chen
Published 2019 · Computer Science

Cite This
Download PDF
Analyze on Scholarcy
Share
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.
This paper references
10.1016/j.cor.2014.04.013
Ecogeography-based optimization: Enhancing biogeography-based optimization with ecogeographic barriers and differentiations
Yu-Jun Zheng (2014)
10.1145/2598394.2602287
Genetic algorithms for evolving deep neural networks
E. David (2014)
10.1109/TNN.2002.804317
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
F. F. Leung (2003)
10.1126/SCIENCE.1127647
Reducing the Dimensionality of Data with Neural Networks
Geoffrey E. Hinton (2006)
10.1016/j.asoc.2015.08.043
Fine-tuning Deep Belief Networks using Harmony Search
J. Papa (2016)
10.1145/1553374.1553453
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
H. Lee (2009)
10.1016/S0092-8240(05)80006-0
A logical calculus of the ideas immanent in nervous activity
W. McCulloch (1990)
10.1007/s00500-013-1209-1
Localized biogeography-based optimization
Yu-Jun Zheng (2014)
10.1109/TEVC.2005.857610
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
J. Liang (2006)
UCI Repository of machine learning databases
Catherine Blake (1998)
10.1016/j.engappai.2010.08.005
Blended biogeography-based optimization for constrained optimization
Haiping Ma (2011)
10.1109/TFUZZ.2010.2098879
A TS-Type Maximizing-Discriminability-Based Recurrent Fuzzy Network for Classification Problems
Gin-Der Wu (2011)
10.1109/TEVC.2008.919004
Biogeography-Based Optimization
Dan Simon (2008)
10.1016/j.neucom.2016.11.018
An evolutionary deep neural network for predicting morbidity of gastrointestinal infections by food contamination
Qin Song (2017)
10.1016/0004-3702(89)90049-0
Connectionist Learning Procedures
Geoffrey E. Hinton (1989)
10.1109/TFUZZ.2009.2034529
Hierarchical Cluster-Based Multispecies Particle-Swarm Optimization for Fuzzy-System Optimization
C. Juang (2010)
Artificial Intelligence: A Guide to Intelligent Systems
M. Negnevitsky (2001)
10.1073/pnas.79.8.2554
Neural networks and physical systems with emergent collective computational abilities.
J. Hopfield (1982)
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Chin-Teng Lin (1996)
10.1145/1390156.1390294
Extracting and composing robust features with denoising autoencoders
Pascal Vincent (2008)
10.1016/j.amc.2006.07.025
A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training
J. Zhang (2007)
10.1109/TFUZZ.2014.2337938
A Hybrid Neuro-Fuzzy Network Based on Differential Biogeography-Based Optimization for Online Population Classification in Earthquakes
Yu-Jun Zheng (2015)
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
P. Vincent (2010)
10.1109/TEVC.2008.927706
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
A. Qin (2009)



Semantic Scholar Logo Some data provided by SemanticScholar