  Run time analysis of evolutionary algorithms recently makes significant
progress in linking algorithm performance to algorithm parameters. However,
settings that study the impact of problem parameters are rare. The recently
proposed W-model provides a good framework for such analyses, generating
pseudo-Boolean optimization problems with tunable properties.
  We initiate theoretical research of the W-model by studying how one of its
properties -- neutrality -- influences the run time of random local search.
Neutrality creates plateaus in the search space by first performing a majority
vote for subsets of the solution candidate and then evaluating the
smaller-dimensional string via a low-level fitness function.
  We prove upper bounds for the expected run time of random local search on
this MAJORITY problem for its entire parameter spectrum. To this end, we
provide a theorem, applicable to many optimization algorithms, that links the
run time of MAJORITY with its symmetric version HASMAJORITY, where a sufficient
majority is needed to optimize the subset. We also introduce a generalized
version of classic drift theorems as well as a generalized version of Wald's
equation, both of which we believe to be of independent interest.
