Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression

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@InProceedings{Burlacu:2019:EUROCAST,

• author = "Bogdan Burlacu and Lukas Kammerer and Michael Affenzeller and Gabriel Kronberger",
• title = "Hash-Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression",
• booktitle = "International Conference on Computer Aided Systems Theory, EUROCAST 2019",
• year = "2019",
• editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia",
• volume = "12013",
• series = "Lecture Notes in Computer Science",
• pages = "361--369",
• address = "Las Palmas de Gran Canaria, Spain",
• month = "17-22 " # feb,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-030-45092-2",
• DOI = "doi:10.1007/978-3-030-45093-9_44",
• abstract = "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.",

}

Genetic Programming entries for Bogdan Burlacu Lukas Kammerer Michael Affenzeller Gabriel Kronberger

Citations