  A major goal of machine learning is to create techniques that abstract away
irrelevant information. The generalisation property of standard Learning
Classifier System (LCS) removes such information at the feature level but not
at the feature interaction level. Code Fragments (CFs), a form of tree-based
programs, introduced feature manipulation to discover important interactions,
but they often contain irrelevant information, which causes structural
inefficiency. XOF is a recently introduced LCS that uses CFs to encode building
blocks of knowledge about feature interaction. This paper aims to optimise the
structural efficiency of CFs in XOF. We propose two measures to improve
constructing CFs to achieve this goal. Firstly, a new CF-fitness update
estimates the applicability of CFs that also considers the structural
complexity. The second measure we can use is a niche-based method of generating
CFs. These approaches were tested on Even-parity and Hierarchical problems,
which require highly complex combinations of input features to capture the data
patterns. The results show that the proposed methods significantly increase the
structural efficiency of CFs, which is estimated by the rule "generality rate".
This results in faster learning performance in the Hierarchical Majority-on
problem. Furthermore, a user-set depth limit for CF generation is not needed as
the learning agent will not adopt higher-level CFs once optimal CFs are
constructed.
