Evolving Modular Abstractions Through Lateralized Learning in Classifier Systems

Conventional classifier systems in evolutionary machine learning often struggle to detect and generalize hierarchical patterns, and thus cannot solve complex real-world problems. Inspired by lateralization and modularity in the biological brain, this extended abstract introduces the developed lateralized classifier system that evolves modular abstractions by decomposing problems into reusable knowledge components. The lateralized system consists of two complementary modules: one (Left Hemispheric Stratagem Module) focused on learning fine-grained, constituent patterns and the other (Right Hemispheric Stratagem Module) on extracting abstract, holistic relationships. Knowledge is encoded using disjunctive normal form-based code fragments and organized into hierarchical concepts stored in a heterogeneous knowledge pool. This structure enables recursive composition and facilitates efficient transfer of building-block knowledge across tasks. Experimental evaluations using complex Boolean problems demonstrate that the lateralized system significantly outperforms conventional learning classifier systems for solving complex problems such as the n-bit Parity problems and 18-bit hierarchical multiplexer problems.