A Self-Adaptive Classifier System
Abstract
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.
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