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.
|Title of host publication
|Computational Intelligence: A Compendium
|John FULCHER, L. C. JAIN
|Number of pages
|Published - 2008
|Studies in Computational Intelligence