Using Denoising Autoencoder Genetic Programming to Control Exploration and Exploitation in Search

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Wittenberg:2022:EuroGP,

• author = "David Wittenberg",
• title = "Using Denoising Autoencoder Genetic Programming to Control Exploration and Exploitation in Search",
• booktitle = "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming",
• year = "2022",
• editor = "Eric Medvet and Gisele Pappa and Bing Xue",
• series = "LNCS",
• volume = "13223",
• publisher = "Springer Verlag",
• address = "Madrid, Spain",
• pages = "102--117",
• month = "20-22 " # apr,
• organisation = "EvoStar, Species",
• note = "Best paper nomination",
• keywords = "genetic algorithms, genetic programming, Estimation of Distribution Algorithms, EDA, Probabilistic Model-Building, Denoising Autoencoders",
• isbn13 = "978-3-031-02055-1",
• DOI = "doi:10.1007/978-3-031-02056-8_7",
• abstract = "Denoising Autoencoder Genetic Programming (DAE-GP) is a novel neural network-based estimation of distribution genetic programming (EDA-GP) algorithm that uses denoising autoencoder long short-term memory networks as a probabilistic model to replace the standard mutation and recombination operators of genetic programming (GP). At each generation, the idea is to flexibly identify promising properties of the parent population and to transfer these properties to the offspring where the DAE-GP uses denoising to make the model robust to noise that is present in the parent population. Denoising partially corrupts candidate solutions that are used as input to the model. The stronger the corruption, the stronger the generalization of the model. In this work, we study how corruption strength affects the exploration and exploitation behavior of the DAE-GP. For a generalization of the royal tree problem (high-locality problem), we find that the stronger the corruption, the stronger the exploration of the solution space. For the given problem, weak corruption resulting in a stronger exploitation of the solution space performs best. However, in more rugged fitness landscapes (low-locality problems), we expect that a stronger corruption resulting in a stronger exploration will be helpful. Choosing the right denoising strategy can therefore help to control the exploration and exploitation behavior in search, leading to an improved search quality.",
• notes = "http://www.evostar.org/2022/eurogp/ Part of \cite{Medvet:2022:GP} EuroGP'2022 held inconjunction with EvoApplications2022 EvoCOP2022 EvoMusArt2022",

}

Genetic Programming entries for David Wittenberg

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