Symbiotic Coevolution of Artificial Neural Networks and Training Data Sets
Among the most important design issues to be addressed to optimize the generalization abilities of trained artificial neural networks (ANNs) are the specific architecture and the composition of the training data set (TDS). Recent work has focused on investigating each of these prerequisites separately. However, some researchers have pointed out the interacting dependencies of ANN topology and the information contained in the TDS. In order to generate coadapted ANNs and TDSs without human intervention we investigate the use of symbiotic (cooperative) coevolution. Independent populations of ANNs and TDSs are evolved by a genetic algorithm (GA), where the fitness of an ANN is equally credited to the TDS it has been trained with. The parallel netGEN system generating generalized multi-layer perceptrons being trained by error-back-propagation has been extended to coevolve TDSs. Empirical results on a simple pattern recognition problem are presented.
Helmut A. Mayer
<helmut@cosy.sbg.ac.at>
Last modified: Oct 6 1998