Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Classifier systems and genetic algorithms
Artificial Intelligence
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms and fitness variance with an application to the automated design of artificial neural networks
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Learning Bayesian networks with local structure
Learning in graphical models
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
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
The anticipatory classifier system and genetic generalization
Natural Computing: an international journal
Proceedings of the 5th International Conference on Genetic Algorithms
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
Bayesian optimization algorithm: from single level to hierarchy
Bayesian optimization algorithm: from single level to hierarchy
Information Sciences: an International Journal - Special issue: Evolutionary computation
Rule-based evolutionary online learning systems: learning bounds, classification, and prediction
Rule-based evolutionary online learning systems: learning bounds, classification, and prediction
Classifier fitness based on accuracy
Evolutionary Computation
Tournament selection: stable fitness pressure in XCS
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Designing efficient exploration with MACS: modules and function approximation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Self-adaptive mutation in XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
Substructural Surrogates for Learning Decomposable Classification Problems
Learning Classifier Systems
Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy
Evolutionary Computation
Performance and efficiency of memetic pittsburgh learning classifier systems
Evolutionary Computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
CoXCS: A Coevolutionary Learning Classifier Based on Feature Space Partitioning
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Estimation of distribution algorithms: from available implementations to potential developments
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Guided rule discovery in XCS for high-dimensional classification problems
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Information Sciences: an International Journal
Extracting and using building blocks of knowledge in learning classifier systems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Comparison of two methods for computing action values in XCS with code-fragment actions
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A combined approach to tackle imbalanced data sets
International Journal of Hybrid Intelligent Systems
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Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are specialized, propagated, and recombined to provide increasingly accurate subsolutions. Recently, it was shown that, as in conventional genetic algorithms (GAs), some problems require efficient processing of subsets of features to find problem solutions efficiently. In such problems, standard variation operators of genetic and evolutionary algorithms used in LCSs suffer from potential disruption of groups of interacting features, resulting in poor performance. This paper introduces efficient crossover operators to XCS by incorporating techniques derived from competent GAs: the extended compact GA (ECGA) and the Bayesian optimization algorithm (BOA). Instead of simple crossover operators such as uniform crossover or one-point crossover, ECGA or BOA-derived mechanisms are used to build a probabilistic model of the global population and to generate offspring classifiers locally using the model. Several offspring generation variations are introduced and evaluated. The results show that it is possible to achieve performance similar to runs with an informed crossover operator that is specifically designed to yield ideal problem-dependent exploration, exploiting provided problem structure information. Thus, we create the first competent LCSs, XCS/ECGA and XCS/BOA, that detect dependency structures online and propagate corresponding lower-level dependency structures effectively without any information about these structures given in advance.