This paper discusses ways of exploiting knowledge extracted from the optimization process, in order to assist an evolutionary solver in its path towards solution. In the context of NK fitness landscapes, we identify two facets of the difficulty of an optimization problem: the intrinsic combinatorial difficulty and its hybridization with random-search. For the experimental part, a particular case of NK fitness landscape is considered; traditional genetic algorithms and Integrated-Adaptive Genetic Algorithms (IAGA), which provide broad adaption mechanisms for most of GA’s components, are compared. We add to IAGA a learn-as-you-go system which allows operators to self-tune their behavior by inspecting the effect they produce on offspring. This system demonstrates that information derived from failures is as valuable as information obtained from positive experience. These results suggest that an appropriately designed adaptive system can be a tool for tackling problem difficulty caused by random-search hybridization.