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arXiv · 1902.00882

Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure

Bogdan Burlacu·Michael Affenzeller·Gabriel Kronberger·Michael Kommenda
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Abstract

Diversity represents an important aspect of genetic programming, being directly correlated with search performance. When considered at the genotype level, diversity often requires expensive tree distance measures which have a negative impact on the algorithm's runtime performance. In this work we introduce a fast, hash-based tree distance measure to massively speed-up the calculation of population diversity during the algorithmic run. We combine this measure with the standard GA and the NSGA-II genetic algorithms to steer the search towards higher diversity. We validate the approach on a collection of benchmark problems for symbolic regression where our method consistently outperforms the standard GA as well as NSGA-II configurations with different secondary objectives.

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Bogdan Burlacu, Michael Affenzeller, Gabriel Kronberger, Michael Kommenda. 2019-02-03. Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure. https://doi.org/10.1109/cec.2019.8790162

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