Abstract
We begin this Chapter with a short discussion of the difficulties of working with and understanding complex systems such as classifier systems, in which we suggest that many models or paradigms of a complex system may need to be considered before we arrive at the most suitable ones. Following this we consider a number of models of classifier systems. We review the rationale behind Holland’s classifier systems and XCS, as presented by their authors. We do not engage in detailed discussions of their ideas, but rather attempt to state in simple terms why they expect these systems to learn. The main point of this part of the Chapter will be that strength and accuracy-based systems differ fundamentally in how they solve problems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2004 Springer-Verlag London
About this chapter
Cite this chapter
Kovacs, T. (2004). How Strength and Accuracy Differ. In: Strength or Accuracy: Credit Assignment in Learning Classifier Systems. Distinguished Dissertations. Springer, London. https://doi.org/10.1007/978-0-85729-416-6_3
Download citation
DOI: https://doi.org/10.1007/978-0-85729-416-6_3
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1058-3
Online ISBN: 978-0-85729-416-6
eBook Packages: Springer Book Archive