ArticleCognitive neurodynamics2016
A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training.
Article in Cognitive neurodynamics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- A new hybrid algorithm for three-stage gene selection based on whale optimization.Scientific reports · 2023Article
- Birdsongs recognition based on ensemble ELM with multi-strategy differential evolution.Scientific reports · 2022Article
- Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification.IET systems biology · 2020Article
- Spoken language identification based on the enhanced self-adjusting extreme learning machine approach.PloS one · 2018Article
- Extreme learning machines for regression based on V-matrix method.Cognitive neurodynamics · 2017Article
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3 authors.
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Abstract
Extreme learning machine (ELM) is a novel and fast learning method to train single layer feed-forward networks. However due to the demand for larger number of hidden neurons, the prediction speed of ELM is not fast enough. An evolutionary based ELM with differential evolution (DE) has been proposed to reduce the prediction time of original ELM. But it may still get stuck at local optima. In this paper, a novel algorithm hybridizing DE and metaheuristic coral reef optimization (CRO), which is called differential evolution coral reef optimization (DECRO), is proposed to balance the explorative power and exploitive power to reach better performance. The thought and the implement of DECRO algorithm are discussed in this article with detail. DE, CRO and DECRO are applied to ELM training respectively. Experimental results show that DECRO-ELM can reduce the prediction time of original ELM, and obtain better performance for training ELM than both DE and CRO.
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