  A key stepping stone in the development of an artificial general intelligence
(a machine that can perform any task), is the production of agents that can
perform multiple tasks at once instead of just one. Unfortunately, canonical
methods are very prone to catastrophic forgetting (CF) - the act of overwriting
previous knowledge about a task when learning a new task. Recent efforts have
developed techniques for overcoming CF in learning systems, but no attempt has
been made to apply these new techniques to evolutionary systems. This research
presents a novel technique, weight protection, for reducing CF in evolutionary
systems by adapting a method from learning systems. It is used in conjunction
with other evolutionary approaches for overcoming CF and is shown to be
effective at alleviating CF when applied to a suite of reinforcement learning
tasks. It is speculated that this work could indicate the potential for a wider
application of existing learning-based approaches to evolutionary systems and
that evolutionary techniques may be competitive with or better than learning
systems when it comes to reducing CF.
