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We propose using the so called Royal Road functions astest functions for cooperative co-evolutionary algorithms (CCEAs). TheRoyal Road functions were created in the early 90's with the aim ofdemonstrating the superiority of genetic algorithms over local searchmethods. Unexpectedly, the opposite was found to be true. The researchdeepened our understanding of the phenomenon of hitchhiking whereunfavorable alleles may become established in the population followingan early association with an instance of a highly fit schema. Here, wetake advantage of the modular and hierarchical structure of the RoyalRoad functions to adapt them to a co-evolutionary setting. Using a multiplepopulation approach, we show that a CCEA easily outperforms astandard genetic algorithm on the Royal Road functions, by naturallyovercoming the hitchhiking effect. Moreover, we found that the optimalnumber of sub-populations for the CCEA is not the same as the numberof components that the function can be linearly separated into, andpropose an explanation for this behavior. We argue that this class offunctions may serve in foundational studies of cooperative co-evolution.