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. A close relationship is also shown 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.
['Fernando G. Lobo', 'Cláudio F. Lima']
On the utility of the multimodal problem generator for assessing the performance of evolutionary algorithms