# Evolving artificial neural networks

> https://ieeexplore.ieee.org/abstract/document/784219

https://ieeexplore.ieee.org/abstract/document/784219

## Abstract

> Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and [evolution](https://wiki.g15e.com/pages/Evolution.txt) with [artificial neural networks](https://wiki.g15e.com/pages/Artificial%20neural%20network.txt) (ANNs) in recent years. This paper: 1) reviews different combinations between ANNs and <evolutionary algorithms> (EAs), including using EAs to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EAs; and 3) points out possible future research directions. It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.