Top-down induction of first-order logical decision trees
Artificial Intelligence
Inductive logic programming: issues, results and the challenge of learning language in logic
Artificial Intelligence - Special issue on applications of artificial intelligence
Machine Learning
Phase Transitions in Relational Learning
Machine Learning
Learning Logical Definitions from Relations
Machine Learning
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Search-intensive concept induction
Evolutionary Computation
Natural coding: a more efficient representation for evolutionary learning
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Handling continuous attributes in an evolutionary inductive learner
IEEE Transactions on Evolutionary Computation
Evolutionary learning of hierarchical decision rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Inductive learning in First-Order Logic (FOL) is a hard task due to both the prohibitive size of the search space and the computational cost of evaluating hypotheses. This paper describes an evolutionary algorithm for concept learning in (a fragment of) FOL. The algorithm, called ECL (for Evolutionary Concept Learner), evolves a population of Horn clauses by repeated selection, mutation and optimization of more fit clauses. ECL relies on four greedy mutation operators for searching the hypothesis space, and employs an optimization phase that follows each mutation. Experimental results show that ECL works well in practice.