  We have recently presented SAFE -- Solution And Fitness Evolution -- a
commensalistic coevolutionary algorithm that maintains two coevolving
populations: a population of candidate solutions and a population of candidate
objective functions. We showed that SAFE was successful at evolving solutions
within a robotic maze domain. Herein we present an investigation of SAFE's
adaptation and application to multiobjective problems, wherein candidate
objective functions explore different weightings of each objective. Though
preliminary, the results suggest that SAFE, and the concept of coevolving
solutions and objective functions, can identify a similar set of optimal
multiobjective solutions without explicitly employing a Pareto front for
fitness calculation and parent selection. These findings support our hypothesis
that the SAFE algorithm concept can not only solve complex problems, but can
adapt to the challenge of problems with multiple objectives.
