Evolutionary Concept Learning in Equational Logic by Chi Shen a Thesis Submitted in Partial Fulfillment of the Requirements of Master of Science in Computer Science University of Rhode Island

Concept learning is a branch of machine learning concerned with learning how to discriminate and categorize things based on positive and negative examples. More specifically, the learning algorithm induces a description of the concept (in some representation language) from a set of positive and negative facts. Inductive logic programming can be considered a subcategory of concept learning where the representation language is first-order logic and the induced descriptions are a set of statements in first-order logic. This problem can be viewed as a search over all possible sentences in the representation language to find those that correctly predict the given examples and generalizes well to unseen examples. Here we consider equational logic as the representation language. Previous work has been done using evolutionary algorithms for search in equational logic with limited success. The purpose of this thesis is to implement a new and better system to solve problems that were unsolvable in the previous implementation. New heuristics have been developed to enhance the speed and success of the search for solutions. This new system has solved a variety of problems, including those that were solved by the previous implementation and many that were not. We have shown that a genetic algorithm is an effective search mechanism for concept learning in the domain of equational logic.