A REVIEW OF EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS

Research on potential interactions between connectionist learning systems, i.e., artificial neural networks (ANNs), and evolutionary search procedures, like genetic algorithms (GAs), has attracted a lot of attention recently. Evolutionary ANNs (EANNs) can be considered as the combination of ANNs and evolutionary search procedures. This article first distinguishes among three kinds of evolution in EANNs, i.e., the evolution of connection weights, of architectures, and of learning rules. Then it reviews each kind of evolution in detail and analyzes critical issues related to different evolutions. The review shows that although a lot of work has been done on the evolution of connection weights and architectures, few attempts have been made to understand the evolution of learning rules. Interactions among different evolutions are seldom mentioned in current research. However, the evolution of learning rules and its interactions with other kinds of evolution, play a vital role in EANNs. Finally, this article briefly describes a general framework for EANNs, which not only includes the aforementioned three kinds of evolution, but also considers interactions among them.