  One of the main reasons for the success of Evolutionary Algorithms (EAs) is
their general-purposeness, i.e., the fact that they can be applied
straightforwardly to a broad range of optimization problems, without any
specific prior knowledge. On the other hand, it has been shown that
incorporating a priori knowledge, such as expert knowledge or empirical
findings, can significantly improve the performance of an EA. However,
integrating knowledge in EAs poses numerous challenges. It is often the case
that the features of the search space are unknown, hence any knowledge
associated with the search space properties can be hardly used. In addition, a
priori knowledge is typically problem-specific and hard to generalize. In this
paper, we propose a framework, called Knowledge Integrated Evolutionary
Algorithm (KIEA), which facilitates the integration of existing knowledge into
EAs. Notably, the KIEA framework is EA-agnostic (i.e., it works with any
evolutionary algorithm), problem-independent (i.e., it is not dedicated to a
specific type of problems), expandable (i.e., its knowledge base can grow over
time). Furthermore, the framework integrates knowledge while the EA is running,
thus optimizing the use of the needed computational power. In the preliminary
experiments shown here, we observe that the KIEA framework produces in the
worst case an 80% improvement on the converge time, w.r.t. the corresponding
"knowledge-free" EA counterpart.
