  A fitness assignment process transforms the features (such as the objective
value) of a candidate solution to a scalar fitness, which then is the basis for
selection. Under Frequency Fitness Assignment (FFA), the fitness corresponding
to an objective value is its encounter frequency in selection steps and is
subject to minimization. FFA creates algorithms that are not biased towards
better solutions and are invariant under all injective transformations of the
objective function value. We investigate the impact of FFA on the performance
of two theory-inspired, state-of-the-art EAs, the Greedy (2+1) GA and the
Self-Adjusting (1+(lambda,lambda)) GA. FFA improves their performance
significantly on some problems that are hard for them. In our experiments, one
FFA-based algorithm exhibited mean runtimes that appear to be polynomial on the
theory-based benchmark problems in our study, including traps, jumps, and
plateaus. We propose two hybrid approaches that use both direct and FFA-based
optimization and find that they perform well. All FFA-based algorithms also
perform better on satisfiability problems than any of the pure algorithm
variants.
