C4.5: programs for machine learning
C4.5: programs for machine learning
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Inference for the Generalization Error
Machine Learning
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Learning theories using estimation distribution algorithms and (reduced) bottom clauses
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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The performance of Evolutionary Algorithms for combinatorial problems can be significantly improved by adding Local Search, thus obtaining a Genetic Local Search (GLS) also called Memetic Algorithm. In this work, we adapt a previous Stochastic Local Search (SLS) algorithm and embed it into a GBML system. The adapted SLS algorithm works as a module of the system that tries to improve a random individual in the population. We perform experiments to evaluate this adapted SLS procedure and results show that this new GLS system is very effective, not losing in any of the 10 UCI datasets tested when compared to the system without the SLS procedure. The system either obtained significantly more accurate concepts using lower number of rules and features or it achieved the same accuracy as the system without the SLS procedure, but reduced the number of rules and features, and also the time taken to develop the solution.