Minisymposium Evolutionary Algorithms: Plateaus Can Be Harder in Multi-Objective Optimization

behavior of evolutionary computation methods for single- and multi-objective problems from a theoretical point of view. Our aim is to analyze the diversity mechanisms that are implicitly used in evolutionary algorithms for multi-objective problems by rigorous runtime analyses. We show that, even if the population size is small, the runtime can be exponential where corresponding single-objective problems are optimized within polynomial time. To illustrate this behavior we analyze a simple plateau function in a first step and extend our result to a class of instances of the well-known SetCover problem.