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
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Machine Learning
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
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
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
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
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
Studying XCS/BOA learning in Boolean functions: structure encoding and random Boolean functions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Do not match, inherit: fitness surrogates for genetics-based machine learning techniques
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Real-Valued LCS Using UNDX for Technology Extraction
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Linkage Learning, Rule Representation, and the Χ-Ary Extended Compact Classifier System
Learning Classifier Systems
Technology Extraction of Expert Operator Skills from Process Time Series Data
Learning Classifier Systems
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Effect of pure error-based fitness in XCS
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
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Michigan-style learning classifier systems (LCSs), such as the accuracy-based XCS system, evolve distributed problem solutions represented by a population of rules. Recently, it was shown that decomposable problems may require effective processing of subsets of problem attributes, which cannot be generally assured with standard crossover operators. A number of competent crossover operators capable of effective identification and processing of arbitrary subsets of variables or string positions were proposed for genetic and evolutionary algorithms. This paper effectively introduces two competent crossover operators to XCS by incorporating techniques from competent genetic algorithms (GAs): the extended compact GA (ECGA) and the Bayesian optimization algorithm (BOA). Instead of applying standard crossover operators, here a probabilistic model of the global population is built and sampled to generate offspring classifiers locally. Various offspring generation methods are introduced and evaluated. Results indicate that the performance of the proposed learning classifier systems XCS/ECGA and XCS/BOA is similar to that of XCS with informed crossover operators that is given all information about problem structure on input and exploits this knowledge using problem-specific crossover operators.