Evolving improved incremental learning schemes for neural network systems

It is well known that incremental learning can often be difficult for traditional neural network systems, due to newly learned information interfering with previously learned information. In this paper, we present simulation results which demonstrate how evolutionary computation techniques can be used to generate neural network incremental learners that exhibit improved performance over existing systems.