Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Evolution of Parallel Cellular Machines: The Cellular Programming Approach
Evolution of Parallel Cellular Machines: The Cellular Programming Approach
Computer
Genetic Programming for Pedestrians
Proceedings of the 5th International Conference on Genetic Algorithms
On Using Constructivism in Neural Classifier Systems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Classification of random boolean networks
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
Toward Optimal Classifier System Performance in Non-Markov Environments
Evolutionary Computation
Journal of Electronic Testing: Theory and Applications
Comparison of tree and graph encodings as function of problem complexity
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Self-adaptive constructivism in Neural XCS and XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A first assessment of the use of extended relational alphabets in accuracy classifier systems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
Fuzzy dynamical genetic programming in XCSF
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Dynamical genetic programming in xcsf
Evolutionary Computation
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A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using a discrete dynamical system representation within the XCS Learning Classifier System. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such discrete dynamical systems within XCS to solve a number of well-known test problems.