Name
Abstract | ||
|---|---|---|
The difference between evolving neural networks and evolving neural network ensembles is that the solution of evolving neural networks is an evolved neural network while the solution of evolving neural network ensemble is an evolved population of neural networks. In the practice of evolving neural network ensemble, it is common that each individual rather the whole population is evaluated. During the evolution, the solution of evolving neural networks would be better and better while it might not be the case for the solution of evolving neural network ensembles. It suggests that the final evolved population might be worse so that it is not wise to choose the final population as a solution. Through experimental studies, this paper gives ideas of how to evolve better populations. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/CEC.2006.1688727 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
evolutionary computation,negative correlation learning,learning (artificial intelligence),evolutionary learning,fitness sharing,hydrid learning system,evolving neural network ensembles,neural nets,neural network,learning artificial intelligence | Nervous system network models,Evolutionary acquisition of neural topologies,Computer science,Stochastic neural network,Recurrent neural network,Time delay neural network,Types of artificial neural networks,Artificial intelligence,Deep learning,Cellular neural network,Machine learning | Conference |
ISBN | Citations | PageRank |
0-7803-9487-9 | 2 | 0.38 |
References | Authors | |
12 | 2 | |