Bid competition and specificity reconsidered
Complex Systems
Boolean Feature Discovery in Empirical Learning
Machine Learning
Perceptron redux: emergence of structure
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Triggered rule discovery in classifier systems
Proceedings of the third international conference on Genetic algorithms
Proceedings of the seventh international conference (1990) on Machine learning
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Classifier Systems and the Animat Problem
Machine Learning
Crafting Papers on Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Preliminary Investigation of Modified XCS as a Generic Data Mining Tool
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Learning and problem-solving with multilayer connectionist systems (adaptive, strategy learning, neural networks, reinforcement learning)
Evolution-assisted discovery of sentinel features in epidemiologic surveillance
Evolution-assisted discovery of sentinel features in epidemiologic surveillance
Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Classifier fitness based on accuracy
Evolutionary Computation
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
Normalization Issues in Mathematical Representations
Proceedings of the 9th AISC international conference, the 15th Calculemas symposium, and the 7th international MKM conference on Intelligent Computer Mathematics
Intrusion detection with evolutionary learning classifier systems
Natural Computing: an international journal
Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy
Evolutionary Computation
Heuristics for resolution in propositional logic
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
A combined approach to tackle imbalanced data sets
International Journal of Hybrid Intelligent Systems
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The performance of a learning classifier system is due to its two main components. First, it evolves new structures by generating new rules in a genetic process; second, it adjusts parameters of existing rules, for example rule prediction and accuracy, in an evaluation step, which is not only important for applying the rules, but also for the genetic process. The two components interleave and in the case of XCS drive the population toward a minimal, fit, non-overlapping population. In this work we attempt to gain new insights as to the relative contributions of the two components. We find that the genetic component has an additional role when using the train/test approach which is not present in online learning. We compare XCS to a system in which the rule set is restricted to the initial random population (XCS-NGA, that is, XCS No Genetic Algorithm). For small Boolean functions we can give XCS-NGA all possible rules of a particular condition length. In online learning, XCS-NGA can, given sufficiently many rules, achieve a surprisingly high classification accuracy, comparable to that of XCS. In a train/test approach, however, XCS generalises better than XCS-NGA and there seem to be limitations of XCS-NGA which cannot be overcome simply by increasing the population size. This illustrates that the requirements of a function approximator tend to differ between reinforcement learning (which is typically online) and concept learning (which is typically train/test).