C4.5: programs for machine learning
C4.5: programs for machine learning
LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning
Machine Learning - Special issue on multistrategy learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Machine Learning
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Learning from Inconsistent and Noisy Data: The AQ18 Approach
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
GA-EDA: hybrid evolutionary algorithm using genetic and estimation of distribution algorithms
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Proceedings of the 8th annual conference on Genetic and evolutionary computation
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The Learnable Evolution Model (LEM) involves alternating periods of optimization and learning, performa extremely well on a range of problems, a specialises in achieveing good results in relatively few function evaluations. LEM implementations tend to use sophisticated learning strategies. Here we continue an exploration of alternative and simpler learning strategies, and try Entropy-based Discretization (ED), whereby, for each parameter in the search space, we infer from recent evaluated samples what seems to be a `good' interval. We find that LEM(ED) provides significant advantages in both solution speed and quality over the unadorned evolutionary algorithm, and is usually superior to CMA-ES when the number of evaluations is limited. It is interesting to see such improvement gained from an easily-implemented approach. LEM(ED) can be tentatively recommended for trial on problems where good results are needed in relatively few fitness evaluations, while it is open to several routes of extension and further sophistication. Finally, results reported here are not based on a modern function optimization suite, but ongoing work confirms that our findings remain valid for non-separable functions.