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This paper expands on work previously conducted on the XCS system using code fragments, which are GP-like trees that encapsulate building blocks of knowledge. The usage of code fragments in the XCS system enabled the solution of previously intractable, complex, boolean problems, e.g. the 135 bit multiplexer domain. However, it was not previously possible to replace functionality at nodes with learned relationships, which restricted scaling to larger problems and related domains. The aim of this paper is to reuse learned rule sets as functions. The functions are to be stored along with the code fragments produced as a solution for a problem. The results show for the first time that these learned functions can be reused in the inner nodes of the code fragment trees. The results are encouraging as there was no statistically significant difference in terms of classification. For the simpler problems the new system XCSCF 2 , required much less instances than the XCSCFC to solve the problems. However, for the more complex problems, the XCSCF 2 required more instances than XCSCFC; but the additional time was not prohibitive for the continued development of this approach. The main contribution of this investigation is that functions can be learned and later reused in the inner nodes of a code fragment tree. This is anticipated to lead to a reduced search space and increased performance both in terms of instances needed to solve a problem and classification accuracy.

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