A new era in computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Basic Algorithms and Operators
Basic Algorithms and Operators
Strength or Accuracy? Fitness Calculation in Learning Classifier Systems
Learning Classifier Systems, From Foundations to Applications
An Algorithmic Description of XCS
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Learning classifier systems: a survey
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Classifier fitness based on accuracy
Evolutionary Computation
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Adaptive artificial datasets to discover the effects of domain features for classification tasks
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Adaptive artificial datasets through learning classifier systems for classification tasks
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Classifying objects and patterns to a certain category is crucial for both humans and machines, so that learnt knowledge may be applied across similar problem instances. Although autonomous learning of patterns by machines has advanced recently, it still requires humans to set up the problem at an appropriate level for the learning technique. If the problem is too complex the system does not learn; conversely, if the problem is too simple the system does not reach its full potential to be able to classify environmental examples. In this work, an automated evolving pattern generator and pattern recognizer has been created for pattern classification problems that can be manipulated autonomously using Learning Classifier Systems (LCSs) at different levels of difficulty. Experiments confirm that both of the agents (e.g. the pattern generation and the pattern classification agent) can be evolved autonomously and co-operatively. The novel contributions in this work enable the effect of domain features on classification performance to become human readable, i.e. possibly determine what features make it difficult for the classification algorithm to learn. This work provides a foundation for a co-evolutionary approach to problem domain creation and the associated learning, such that the agents will trigger evolution when necessary.