Adaptable multiple neural networks using evolutionary computation

The architecture of an artificial neural network has a significant influence on its performance. For a given problem, the proper architecture is found by trial and error. This approach is time consuming and may not always produce the optimal network. In this reason, we can apply the evolutionary computation such as genetic algorithm to implement the automation of network's structure as well as the biological inspiration in neural networks to successfully adapt varying input environment. Moreover, we examine the performance of combining multiple evolving networks that are more likely to model the neurophysiology of the human brain. In the combining module, all individual networks or selected individual networks in the population are used. Also, the dynamic data set is used to provide the networks with diversity and generalization. In this model, each evolving individual network is designed to have a specific feature set and neuron connection links for given data. Then, the results are combined through the combining module to improve the generalization performance of the single network. The Iris and Austrian credit data are used in the experiment.