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Learning multimodal parameters: A bare-bones niching differential evolution approach
Gong Y.-J.1; Zhang J.1; Zhou Y.2
2018-07-01
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
ISSN21622388 2162237X
Volume29Issue:7Pages:2944-2959
Abstract

Most learning methods contain optimization as a substep, where the nondifferentiability and multimodality of objectives push forward the interplay of evolutionary optimization algorithms and machine learning models. The recently emerged evolutionary multimodal optimization (MMOP) technique enables the learning of diverse sets of effective parameters for the models simultaneously, providing new opportunities to the applications requiring both accuracy and diversity, such as ensemble, interactive, and interpretive learning. Targeting at locating multiple optima simultaneously in the multimodal landscape, this paper develops an efficient neighborhood-based niching algorithm. Bare-bones differential evolution is used as the baseline. Further, using Gaussian mutation with local mean and standard deviations, the neighborhoods capture niches that match well with the contours of peaks in the landscape. To increase diversity and enhance global exploration, the proposed algorithm embeds a diversity preserving operator to reinitialize converged or overlapped neighborhoods. The experimental results verify that the proposed algorithm has superior and consistent performance for a wide range of MMOP problems. Further, the algorithm has been successfully applied to train neural network ensembles, which validates its effectiveness and benefits of learning multimodal parameters.

KeywordFitness Landscape Gaussian Model Multimodal Optimization (Mmop) Neighborhood Strategy Neural Network Ensemble (Nne) Niching
DOI10.1109/TNNLS.2017.2708712
URLView the original
Indexed BySCI
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000436420400023
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Cited Times [WOS]:7   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.South China University of Technology
2.Universidade de Macau
Recommended Citation
GB/T 7714
Gong Y.-J.,Zhang J.,Zhou Y.. Learning multimodal parameters: A bare-bones niching differential evolution approach[J]. IEEE Transactions on Neural Networks and Learning Systems,2018,29(7):2944-2959.
APA Gong Y.-J.,Zhang J.,&Zhou Y..(2018).Learning multimodal parameters: A bare-bones niching differential evolution approach.IEEE Transactions on Neural Networks and Learning Systems,29(7),2944-2959.
MLA Gong Y.-J.,et al."Learning multimodal parameters: A bare-bones niching differential evolution approach".IEEE Transactions on Neural Networks and Learning Systems 29.7(2018):2944-2959.
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