Removing irrelevant features in neural network classification using evolutionary computations

Evolutionary artificial neural networks (EANN) are a new paradigm that refers to a special class of artificial neural networks (ANN) in which evolution is another fundamental form of adaptation in addition to learning. Evolution can be introduced at various levels of ANN. It can be used to evolve weights, architectures and learning parameters. Evolutionary computations are population-based search methods that have shown promise in many similarly complex tasks. This paper presents an application of evolutionary programming for simultaneously inducing the input structure and weights evolving for multilayer feed-forward perceptrons (MLP) with standard sigmoidal activation function.