This paper is concerned with the simultaneous evolution of artificial neural network (ANN) architectures and weights. The current practice in evolving ANN's is to choose the best ANN in the last generation as the final result. This paper proposes a different approach to form the final result by combining all the individuals in the last generation in order to make best use of all the information contained in the whole population. This approach regards a population of ANN's as an ensemble and uses a combination method to integrate them. Although there has been some work on integrating ANN modules , , little has been done in evolutionary learning to make best use of its population information. Four linear combination methods have been investigated in this paper to illustrate our ideas. Three real-world data sets have been used in our experimental studies, which show that the recursive least-square (RLS) algorithm always produces an integrated system that outperforms the best individual. The results confirm that a population contains more information than a single individual. Evolutionary learning should exploit such information to improve generalization of learned systems. © 1998 IEEE.
|Number of pages
|IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
|Published - Jun 1998
Bibliographical noteThis work was supported in part by the Australian Research Council through its small grant scheme.
- Behavioral evolution
- Evolutionary artificial neural networks
- Evolutionary programming
- Module combination
- Population-based learning