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Learning classifier systems (LCSs) originated from cognitive-science researchbut migrated such that LCS became powerful classification techniques. ModernLCSs can be used to extract building blocks of knowledge to solve moredifficult problems in the same or a related domain. Recent works on LCSs showedthat the knowledge reuse through the adoption of Code Fragments, GP-liketree-based programs, into LCSs could provide advances in scaling. However,since solving hard problems often requires constructing high-level buildingblocks, which also results in an intractable search space, a limit of scalingwill eventually be reached. Inspired by human problem-solving abilities, XCSCF*can reuse learned knowledge and learned functionality to scale to complexproblems by transferring them from simpler problems using layered learning.However, this method was unrefined and suited to only the Multiplexer problemdomain. In this paper, we propose improvements to XCSCF* to enable it to berobust across multiple problem domains. This is demonstrated on the benchmarksMultiplexer, Carry-one, Majority-on, and Even-parity domains. The required baseaxioms necessary for learning are proposed, methods for transfer learning inLCSs developed and learning recast as a decomposition into a series ofsubordinate problems. Results show that from a conventional tabula rasa, withonly a vague notion of what subordinate problems might be relevant, it ispossible to capture the general logic behind the tested domains, so theadvanced system is capable of solving any individual n-bit Multiplexer, n-bitCarry-one, n-bit Majority-on, or n-bit Even-parity problem.

A Layered Learning Approach to Scaling in Learning Classifier Systems for Boolean Problems