Introducing Emergent Loose Modules into the Learning Process of a Linear Genetic Programming System

Modularity and building blocks have drawn attention from the genetic programming (GP) community for a long time. The results are usually twofold: a hierarchical evolution with adequate building block reuse can accelerate the learning process, but rigidly defined and excessively employed modules may also counteract the expected advantages by confining the reachable search space. In this work, we introduce the concept of emergent loose modules based on a new linear GP system, prefix gene expression programming (P-GEP), in an attempt to balance between the stochastic exploration and the hierarchical construction for the optimal solutions. Emergent loose modules are dynamically produced by the evolution, and are reusable as sub-functions in later generations. The proposed technique is fully illustrated with a simple symbolic regression problem. The initial experimental results suggest it is a flexible approach in identifying the evolved regularity and the emergent loose modules are critical in composing the best solutions