Evolutionary ensembles with negative correlation learning

Yong LIU, Xin YAO, Tetsuya HIGUCHI

Research output: Journal PublicationsJournal Article (refereed)peer-review

357 Citations (Scopus)


Based on negative correlation learning and evolutionary learning, this brief paper presents evolutionary ensembles with negative correlation learning (EENCL) to address the issues of automatic determination of the number of individual neural networks (NNs) in an ensemble and the exploitation of the interaction between individual NN design and combination. The idea of EENCL is to encourage different individual NNs in the ensemble to learn different parts or aspects of the training data so that the ensemble can learn better the entire training data. The cooperation and specialization among different individual NNs are considered during the individual NN design. This provides an opportunity for different NNs to interact with each other and to specialize. Experiments on two real-world problems demonstrate that EENCL can produce NN ensembles with good generalization ability.
Original languageEnglish
Pages (from-to)380-387
Number of pages8
JournalIEEE Transactions on Evolutionary Computation
Issue number4
Publication statusPublished - 2000
Externally publishedYes


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