  Evolutionary algorithms have been successfully applied to a variety of
optimisation problems in stationary environments. However, many real world
optimisation problems are set in dynamic environments where the success
criteria shifts regularly. Population diversity affects algorithmic
performance, particularly on multiobjective and dynamic problems. Diversity
mechanisms are methods of altering evolutionary algorithms in a way that
promotes the maintenance of population diversity. This project intends to
measure and compare the performance effect a variety of diversity mechanisms
have on an evolutionary algorithm when facing an assortment of dynamic
problems.
