  This paper presents algorithmic and empirical contributions demonstrating
that the convergence characteristics of a co-evolutionary approach to tackle
Multi-Objective Games (MOGs) with postponed preference articulation can often
be hampered due to the possible emergence of the so-called Red Queen effect.
Accordingly, it is hypothesized that the convergence characteristics can be
significantly improved through the incorporation of memetics (local solution
refinements as a form of lifelong learning), as a promising means of mitigating
(or at least suppressing) the Red Queen phenomenon by providing a guiding hand
to the purely genetic mechanisms of co-evolution. Our practical motivation is
to address MOGs of a time-sensitive nature that are characterized by
computationally expensive evaluations, wherein there is a natural need to
reduce the total number of true function evaluations consumed in achieving good
quality solutions. To this end, we propose novel enhancements to
co-evolutionary approaches for tackling MOGs, such that memetic local
refinements can be efficiently applied on evolved candidate strategies by
searching on computationally cheap surrogate payoff landscapes (that preserve
postponed preference conditions). The efficacy of the proposal is demonstrated
on a suite of test MOGs that have been designed.
