Inducing Logic Programs With Genetic Algorithms: The Genetic Logic Programming System
IEEE Expert: Intelligent Systems and Their Applications
Learning Logical Definitions from Relations
Machine Learning
ILP Experiments in Detecting Traffic Problems
ECML '98 Proceedings of the 10th European Conference on Machine Learning
An Experimental Evaluation of Coevolutive Concept Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Detecting Traffic Problems with ILP
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Search-intensive concept induction
Evolutionary Computation
Non-universal suffrage selection operators favor population diversity in genetic algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Handling continuous attributes in an evolutionary inductive learner
IEEE Transactions on Evolutionary Computation
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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.