This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANN's). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP). Unlike most previous studies on evolving ANN's, this paper puts its emphasis on evolving ANN's behaviors. This is one of the primary reasons why EP is adopted. Five mutation operators proposed in EPNet reflect such an emphasis on evolving behaviors. Close behavioral links between parents and their offspring are maintained by various mutations, such as partial training and node splitting. EPNet evolves ANN's architectures and connection weights (including biases) simultaneously in order to reduce the noise in fitness evaluation. The parsimony of evolved ANN's is encouraged by preferring node/connection deletion to addition. EPNet has been tested on a number of benchmark problems in machine learning and ANN's, such as the parity problem, the medical diagnosis problems (breast cancer, diabetes, heart disease, and thyroid), the Australian credit card assessment problem, and the Mackey-Glass time series prediction problem. The experimental results show that EPNet can produce very compact ANN's with good generalization ability in comparison with other algorithms. © 1997 IEEE.
Bibliographical noteThis work was supported by the Australian Research Council through its small grant scheme.
- Evolution of behaviors
- Evolutionary programming
- Neural-network design