Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
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
Classifiers that approximate functions
Natural Computing: an international journal
Evolutionary Computation
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
XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Extracted global structure makes local building block processing effective in XCS
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The compact classifier system: motivation, analysis, and first results
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
EDA-RL: estimation of distribution algorithms for reinforcement learning problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Performance and efficiency of memetic pittsburgh learning classifier systems
Evolutionary Computation
Analysing bioHEL using challenging boolean functions
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Self-adaptation of parameters in a learning classifier system ensemble machine
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
A novel classification learning framework based on estimation of distribution algorithms
International Journal of Computing Science and Mathematics
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Recently, studies with the XCS classifier system on Boolean functions have shown that in certain types of functions simple crossover operators can lead to disruption and, consequently, a more effective recombination mechanism is required. Simple crossover operators were replaced by recombination based on estimation of distribution algorithms (EDAs). The combination showed that XCS with such a statistics-based crossover operator can solve challenging hierarchical functions more efficiently. This study elaborates the gained competence further investigating the coding scheme for the EDA component (BOA in our case) of XCS as well as performance in randomly generated Boolean function problems. Results in hierarchical Boolean functions show that the originally used 2-bit coding scheme induces a certain learning bias that stresses additional diversity in the evolving XCS population. A 1-bit coding scheme as well as a restricted 2-bit coding scheme confirm the suspected bias. The alternative encodings decrease the unnecessary bias towards specificity and increase performance robustness. The paper concludes with a discussion on the challenges ahead for XCS in Boolean function problems as well as on the implications of the obtained results for real-valued and multiple-valued classification problems, multi-step problems, and function approximation problems.