Evolving artificial neural network ensembles

Md. Monirul ISLAM, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsBook ChapterResearchpeer-review

12 Citations (Scopus)


Artificial neural networks (ANNs) and evolutionary algorithms (EAs) are both abstractions of natural processes. In the mid 1990s, they were combined into a computational model in order to utilize the learning power of ANNs and adaptive capabilities of EAs. Evolutionary ANNs (EANNs) is the outcome of such a model. They refer to a special class of ANNs in which evolution is another fundamental form of adaptation in addition to learning [52-57]. The essence of EANNs is their adaptability to a dynamic environment. The two forms of adaptation in EANNs - namely evolution and learning - make their adaptation to a dynamic environment much more effective and efficient. In a broader sense, EANNs can be regarded as a general framework for adaptive systems - in other words, systems that can change their architectures and learning rules appropriately without human intervention. © 2008 Springer-Verlag Berlin Heidelberg.
Original languageEnglish
Title of host publicationComputational Intelligence: A Compendium
EditorsJohn FULCHER, L. C. JAIN
Number of pages30
ISBN (Electronic)9783540782933
ISBN (Print)9783540782926
Publication statusPublished - 2008
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503


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