Communications of the ACM
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
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
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Machine Learning
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Stochastic Hillclimbing as a Baseline Method for
Stochastic Hillclimbing as a Baseline Method for
Bayesian optimization algorithm: from single level to hierarchy
Bayesian optimization algorithm: from single level to hierarchy
The equation for response to selection and its use for prediction
Evolutionary Computation
New approach for extracting knowledge from the XCS learning classifier system
International Journal of Hybrid Intelligent Systems
Connection Science - Evolutionary Learning and Optimisation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Using over-sampling in a Bayesian classifier EDA to solve deceptive and hierarchical problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolutionary bayesian classifier-based optimization in continuous domains
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Hi-index | 0.00 |
This paper explores how inductive machine learning can guide the breeding process of evolutionary algorithms for black-box function optimization. In particular, decision trees are used to identify the underlying characteristics of good and bad individuals, using the mined knowledge for wise breeding purposes. Inductive learning is complemented with statistical learning in order to define the breeding process. The proposed evolutionary process optimizes the fitness function in a dual manner, both maximizing and minimizing it. The paper also summarize some tuning and population sizing issues, as well as some preliminary results obtained using the proposed algorithm.