Assessing the Effectiveness of Incorporating Knowledge in an Evolutionary Concept Learner

Classical methods for Inductive Concept Learning (ICL) rely mostly on using specific search strategies, such as hill climbing and inverse resolution. These strategies have a great exploitation power, but run the risk of being incapable of escaping from local optima. An alternative approach to ICL is represented by Evolutionary Algorithms (EAs). EAs have a great exploration power, thus they have the capability of escaping from local optima, but their exploitation power is rather poor. These observations suggest that the two approaches are applicable to partly complementary classes of learning problems. More important, they indicate that a system incorporating features from both approaches could benefit from the complementary qualities of the approaches. In this paper we experimentally validate this statement. To this end, we incorporate different search strategies in a framework based on EAs for ICL. Results of experiments show that incorporating standard search strategies helps the EA in achieving better results.