Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Lookahead planning and latent learning in a classifier system
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Evolving artificial intelligence
Evolving artificial intelligence
Adding “foveal vision” to Wilson's animat
Adaptive Behavior
Adding temporary memory to ZCS
Adaptive Behavior
Robot Shaping: An Experiment in Behavior Engineering
Robot Shaping: An Experiment in Behavior Engineering
Classifier Systems and the Animat Problem
Machine Learning
Knowledge Growth in an Artificial Animal
Proceedings of the 1st International Conference on Genetic Algorithms
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
A Self-Adaptive Classifier System
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Simple Markov Models of the Genetic Algorithm in Classifier Systems: Multi-step Tasks
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
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
Genetic Programming and Evolvable Machines
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The use and benefits of self-adaptive mutation operators are well-known within evolutionary computing. In this paper we examine the use of self-adaptive mutation in Michigan-style Classifier Systems with the aim of improving their performance as controllers for autonomous mobile robots. Initially, we implement the operator in the ZCS classifier and examine its performance in two "animat" environments. It is shown that, although no significant increase in performance is seen over results presented in the literature using a fixed rate of mutation, the operator adapts to approximately this rate regardless of the initial range.