Abstract
Neural networks suffer from interference when they are evolved for different and unrelated tasks.
This interference causes the networks to forget previously learned behaviours and worsens the
more the network is trained in the new task, preventing networks being able to engage in such
different unrelated tasks.
This report proposes that a cause for this may be that the regular ANN architecture is too
simple and that some additional enhancements may be needed. It is then proposed that modularity
could be used for such purpose, protecting network structures responsible for particular behaviours.
Further, it is proposed a selection scheme intended to handle the selection of modules based on
input to the primary network.
This module-with-selection scheme is tested and shows that modularity provide some
benefits such as simplifying the search space and also enabling symmetric and repeating structures
through module connectivity and reuse. The selection mechanism is also shown to be evolvable,
something not readily apparent.
It is further suggested that this selection mechanism can in the future enable networks to
operate in fractured domains. Also the scheme has support for module within module structures,
enabling a bottom-up approach to constructing networks through incremental evolution, and with
the modules acting as safeguards against interference as the network becomes more complex.