Memetic Networks are a new class of population-based optimization algorithms that makes use of an underlying network to structure information flow between individuals representing points in the search space. Its main characteristic is the possibility of aggregating several solutions in order to compose new ones and the use of an explicit network to aid search. Algorithms from this class can be used to relate network properties to search performance in optimization tasks. We propose and report on algorithms applied to several benchmark optimization problems. We further show how some network properties - in particular, the existence of hubs - can influence the algorithm's performance.