Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Robot Shaping: An Experiment in Behavior Engineering
Robot Shaping: An Experiment in Behavior Engineering
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-criteria Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Consideration of Multiple Objectives in Neural Learning Classifier Systems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Bounding the effect of noise in multiobjective learning classifier systems
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
An analysis of generalization in the xcs classifier system
Evolutionary Computation
Bounding the population size in XCS to ensure reproductive opportunities
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
On the effects of node duplication and connection-oriented constructivism in neural XCSF
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Self-adaptive constructivism in Neural XCS and XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Using the XCS classifier system for portfolio allocation of MSCI index component stocks
Expert Systems with Applications: An International Journal
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We investigate the performance of a learning classifier system in some simple multi-objective, multi-step maze problems, using both random and biased action-selection policies for exploration. Results show that the choice of action-selection policy can significantly affect the performance of the system in such environments. Further, this effect is directly related to population size, and we relate this finding to recent theoretical studies of learning classifier systems in single-step problems.