Thinking Outside the Template with Modular GP-GOMEA
Joe Harrison, Peter A.N. Bosman, Tanja Alderliesten

TL;DR
This paper introduces a modular approach to GP-GOMEA for symbolic regression, enabling the evolution of multiple subexpressions simultaneously, which improves interpretability and often enhances accuracy over traditional fixed-template methods.
Contribution
The paper proposes a modular representation for GP-GOMEA that allows multiple trees to be evolved as subexpressions, increasing flexibility and interpretability in symbolic regression.
Findings
Modular GP-GOMEA generally outperforms single-template GP-GOMEA.
The approach uncovers ground-truth expressions faster on synthetic datasets.
Enhanced interpretability through piece-wise understanding of subexpressions.
Abstract
The goal in Symbolic Regression (SR) is to discover expressions that accurately map input to output data. Because often the intent is to understand these expressions, there is a trade-off between accuracy and the interpretability of expressions. GP-GOMEA excels at producing small SR expressions (increasing the potential for interpretability) with high accuracy, but requires a fixed tree template, which limits the types of expressions that can be evolved. This paper presents a modular representation for GP-GOMEA that allows multiple trees to be evolved simultaneously that can be used as (functional) subexpressions. While each tree individually is constrained to a (small) fixed tree template, the final expression, if expanded, can exhibit a much larger structure. Furthermore, the use of subexpressions decomposes the original regression problem and opens the possibility for enhanced…
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Taxonomy
TopicsAI-based Problem Solving and Planning · Intelligent Tutoring Systems and Adaptive Learning · Big Data and Business Intelligence
