Using implicit multi-objectives properties to mitigate against forgetfulness in coevolutionary algorithms

It had been noticed that, while coevolutionary computational systems have only a single objective when evaluating, there is a subtle multi-objective aspect to evaluation since different pairings can be thought of as different objectives (all in support of the single original objective). Previously researchers used this to identify pairings of individuals during evaluation within a single generation. However, because of the problems of forgetfulness and the Red-Queen effect, this does not allow for the proper control that the technique promises. In this research, this implicit multi-objective approach is extended to function between generations as well as within. This makes it possible to implement a more powerful form of elitism as well as mitigate against some of the pathologies of Coevolutionary systems that forgetfulness and the Red-Queen effect engender, thus providing more robust solutions.