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The complexity of fitness landscapes, e.g., in terms of multimodalities and presence of plateaus, typical of many realworld optimization problems coupled with the limitations imposed by the No Free Lunch Theorem, are strong mitigating factors for domain-specific Memetic Computing approaches. More specifically, since the No Free Lunch Theorem proves that the performance of each algorithm over all the possible problems is the same, there is no longer a reason to discuss which algorithm is universally better or worse. Thus, instead of trying to propose universally applicable algorithms, researchers in recent years have started to propose algorithms which are tailored specific to the problems in hand. In this context, the Memetic Computing paradigm offers the possibility of flexibly designing domain-specific optimization algorithms by integrating and coordinating algorithmic components capable of dealing with difficulties specifically related to the decision space and fitness landscape of a given problem. This thematic issue deals with a set of problem difficulties which are of great interest in an industrial context, i.e., uncertainties in the fitness function, and gathers novel Memetic Computing approaches which attempt to solve this set of problems. It addresses those optimization problems characterized by a complex fitness landscape and uncertain environments. An optimization problem contains uncertainties

Guest editorial: Memetic Computing in the presence of uncertainties