Letter Recognition Using Holland-Style Adaptive Classifiers
Machine Learning
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolving artificial intelligence
Evolving artificial intelligence
Adding temporary memory to ZCS
Adaptive Behavior
Self-Adaptive Mutation in ZCS Controllers
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
Strength or Accuracy? Fitness Calculation in Learning Classifier Systems
Learning Classifier Systems, From Foundations to Applications
A Self-Adaptive Classifier System
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Classifiers that approximate functions
Natural Computing: an international journal
Evolutionary Computation
Genetic Programming and Evolvable Machines
Self-adaptive mutation in XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
On Lookahead and Latent Learning in Simple LCS
Learning Classifier Systems
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Self-adaptation of learning rate in XCS working in noisy and dynamic environments
Computers in Human Behavior
Self-adaptation of parameters in a learning classifier system ensemble machine
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
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Self-adaptation has been used extensively to control parameters in various forms of evolutionary computation. The concept was first introduced with evolutionary strategies and it is now often used to control genetic algorithms. This paper describes the addition of a self-adaptive mutation rate and learning rate to the XCS classifier system. Self-adaptation has been used before in the strength based learning classifier system ZCS. This self-adaptive ZCS demonstrated clear performance improvements in a dynamic Woods environment and stable adaptation of its reinforcement learning parameters. In this paper experiments with XCS are carried out in Woods 2, a truncated version of the Woods 14 environment and a dynamic Woods environment. Performance of XCS in the dynamic Woods 14 environment is good with little loss of performance when the environment is perturbed. Use of an adaptive mutation rate does not help or improve on this behavior. XCS has already been shown to perform poorly in the Woods 14 environment, and other long rule chain environments. Use of an adaptive mutation rate is shown to increase performance significantly in these long rule chain environments. Attempts to also self-adapt the learning rate in Woods 14-12 fail to achieve satisfactory system performance.