EVOLVING NEURAL NETWORKS THAT SUFFER MINIMAL CATASTROPHIC FORGETTING

Catastrophic forgetting is a well-known failing of many neural network systems whereby training on new patterns causes them to forget previously learned patterns. Humans have evolved mechanisms to minimize this problem, and in this paper we present our preliminary attempts to use simulated evolution to generate neural networks that suffer significantly less from catastrophic forgetting than traditionally formulated networks.