Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression
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Abstract (arXiv)
We introduce in this paper a runtime-efficient tree hashing algorithm for the identification of isomorphic subtrees, with two important applications in genetic programming for symbolic regression: fast, online calculation of population diversity and algebraic simplification of symbolic expression trees. Based on this hashing approach, we propose a simple diversity-preservation mechanism with promising results on a collection of symbolic regression benchmark problems.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 0 of its 13 references that are in the map. First posted on arXiv 22 Jul 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
5 papers.
- 2022Exhaustive Symbolic Regressioncited by 29
- 2021Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplicationcited by 17
- 2025Improving Genetic Programming for Symbolic Regression with Equality Graphscited by 5
- 2024Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashingcited by 2
- 2026Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regressioncited by 0
- 2026Evaluation of Population Initialization Methods for Genetic Programming-based Symbolic Regression30 Jun 2026
- 2025Improving Genetic Programming for Symbolic Regression with Equality Graphs29 Jan 2025
- 2024Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing8 Apr 2024
- 2022Exhaustive Symbolic Regression21 Nov 2022
- 2021Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication28 Sep 2021