  This paper looks in detail at how an evolutionary algorithm attempts to solve
instances from the multimodal problem generator. The paper shows that in order
to consistently reach the global optimum, an evolutionary algorithm requires a
population size that should grow at least linearly with the number of peaks. It
is also shown a close relationship between the supply and decision making
issues that have been identified previously in the context of population sizing
models for additively decomposable problems.
  The most important result of the paper, however, is that solving an instance
of the multimodal problem generator is like solving a peak-in-a-haystack, and
it is argued that evolutionary algorithms are not the best algorithms for such
a task. Finally, and as opposed to what several researchers have been doing, it
is our strong belief that the multimodal problem generator is not adequate for
assessing the performance of evolutionary algorithms.

Neural and Evolutionary Computing