Abstract
While the previous chapter gave details of the mathematical description of XCSF, this chapter sheds light on the resulting behavior. XCS, the big brother of XCSF, has already been analyzed thoroughly. While a large part of the theory also applies to XCSF, there are differences that require a different viewpoint. The present chapter reviews the information relevant for XCSF from [16, 17, 13] with the goal to illustrate how and why the algorithm works.
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© 2014 Springer Fachmedien Wiesbaden
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Stalph, P. (2014). How and Why XCSF works. In: Analysis and Design of Machine Learning Techniques. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-04937-9_5
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DOI: https://doi.org/10.1007/978-3-658-04937-9_5
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Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-04936-2
Online ISBN: 978-3-658-04937-9
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