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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Burlacu:2019:CEC,

• author = "B. Burlacu and M. Affenzeller and G. Kronberger and M. Kommenda",
• booktitle = "2019 IEEE Congress on Evolutionary Computation (CEC)",
• title = "Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure",
• year = "2019",
• pages = "2175--2182",
• month = jun,
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/CEC.2019.8790162",
• 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.",
• notes = "Also known as \cite{8790162}",

}

Genetic Programming entries for Bogdan Burlacu Michael Affenzeller Gabriel Kronberger Michael Kommenda

Citations