Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Bucket brigade performance: II. Default hierarchies
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Bid competition and specificity reconsidered
Complex Systems
CSM: A Computational Model of Cumulative Learning
Machine Learning - Special issue on genetic algorithms
Letter Recognition Using Holland-Style Adaptive Classifiers
Machine Learning
Using transputers to increase speed and flexibility of genetics-based machine learning systems
Euromicro 91 Proceedings of the seventeenth Euromicro conference on Software and hardware : specification and design: specification and design
Learning to control an autonomous robot by distributed genetic algorithms
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Learning Sequential Decision Rules Using Simulation Models and Competition
Machine Learning - Special issue on genetic algorithms
A Tale of Two Classifier Systems
Machine Learning
Classifier Systems that Learn Internal World Models
Machine Learning
The Emergence of Default Hierarchies in Learning Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
A Critical Review of Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
A Learning Classifier Systems Bibliography
Learning Classifier Systems, From Foundations to Applications
A Roadmap to the Last Decade of Learning Classifier System Research
Learning Classifier Systems, From Foundations to Applications
Using Classifier Systems as Adaptive Expert Systems for Control
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
YACS: Combining Dynamic Programming with Generalization in Classifier Systems
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Zcs: A zeroth level classifier system
Evolutionary Computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
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It is well known that standard learning classifier systems, when applied to many different domains, exhibit a number of problems: payoff oscillation, difficulty in regulating interplay between the reward system and the background genetic algorithm (GA), rule chains' instability, default hierarchies' instability, among others. ALECSYS is a parallel version of a standard learning classifier system (CS) and, as such, suffers from these same problems. In this paper we propose some innovative solutions to some of these problems. We introduce the following original features. Mutespec is a new genetic operator used to specialize potentially useful classifiers. Energy is a quantity introduced to measure global convergence to apply the genetic algorithm only when the system is close to a steady state. Dynamic adjustment of the classifiers set cardinality speeds up the performance phase of the algorithm. We present simulation results of experiments run in a simulated two-dimensional world in which a simple agent learns to follow a light source.