Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
On Using Constructivism in Neural Classifier Systems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Accuracy-based Neuro And Neuro-fuzzy Classifier Systems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Using gene deletion and gene duplication in evolution strategies
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Classifier systems that compute action mappings
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
An analysis of generalization in the xcs classifier system
Evolutionary Computation
Self-adaptive constructivism in Neural XCS and XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Three architectures for continuous action
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Analysing the evolvability of neural network agents through structural mutations
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Synapsing Variable-Length Crossover: Meaningful Crossover for Variable-Length Genomes
IEEE Transactions on Evolutionary Computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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For artificial entities to achieve high degrees of autonomy they will need to display appropriate adaptability. In this sense adaptability includes representational flexibility guided by the environment at any given time. This paper presents the use of constructivism-inspired mechanisms within a neural learning classifier system which exploits parameter self-adaptation as an approach to realize such behavior. Various network growth/regression mechanisms are implemented and their performances compared. The system uses a rule structure in which each is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the system.