Interaction-Transformation Evolutionary Algorithm for Symbolic Regression
This addresses the problem of complex and noisy search spaces in symbolic regression for researchers and practitioners, though it appears incremental as it builds on existing evolutionary approaches.
The paper tackled symbolic regression by introducing the Interaction-Transformation representation to simplify the search space, and the evolutionary algorithm achieved better approximations on real-world datasets compared to traditional and state-of-the-art methods.
The Interaction-Transformation (IT) is a new representation for Symbolic Regression that restricts the search space into simpler, but expressive, function forms. This representation has the advantage of creating a smoother search space unlike the space generated by Expression Trees, the common representation used in Genetic Programming. This paper introduces an Evolutionary Algorithm capable of evolving a population of IT expressions supported only by the mutation operator. The results show that this representation is capable of finding better approximations to real-world data sets when compared to traditional approaches and a state-of-the-art Genetic Programming algorithm.