Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Evolving artificial intelligence
Evolving artificial intelligence
Adding temporary memory to ZCS
Adaptive Behavior
Classifier Systems and the Animat Problem
Machine Learning
Self-Adaptive Mutation in ZCS Controllers
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
Knowledge Growth in an Artificial Animal
Proceedings of the 1st International Conference on Genetic Algorithms
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
On Using Constructivism in Neural Classifier Systems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
TCS Learning Classifier System Controller on a Real Robot
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
On Lookahead and Latent Learning in Simple LCS
Learning Classifier Systems
On Dynamical Genetic Programming: Random Boolean Networks in Learning Classifier Systems
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
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The use and benefits of self-adaptive parameters, particularly mutation, are well-known within evolutionary computing. In this paper we examine the use of parameter self-adaptation in Michigan-style Classifier Systems with the aim of improving their performance and ease of use. We implement a fully self-adaptive ZCS classifier and examine its performance in a multi-step environment. It is shown that the mutation rate, learning rate, discount factor and tax rate can be developed along with an appropriate solution/rule-base, resulting in improved performance over results using fixed rate parameters. We go on to show that the benefits of self-adaptation are particularly marked in non-stationary environments.