Gutenberg Open Science: Denoising autoencoder genetic programming : strategies to control exploration and exploitation in search

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-9806

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DC Field	Value	Language
dc.contributor.author	Wittenberg, David	-
dc.contributor.author	Rothlauf, Franz	-
dc.contributor.author	Gagné, Christian	-
dc.date.accessioned	2023-12-15T11:01:59Z	-
dc.date.available	2023-12-15T11:01:59Z	-
dc.date.issued	2023	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/9824	-
dc.description.abstract	Denoising autoencoder genetic programming (DAE-GP) is a novel neural network-based estimation of distribution genetic programming approach that uses denoising autoencoder long short-term memory networks as a probabilistic model to replace the standard mutation and recombination operators of genetic programming. At each generation, the idea is to capture promising properties of the parent population in a probabilistic model and to use corruption to transfer variations of these properties to the offspring. This work studies the influence of corruption and sampling steps on search. Corruption partially mutates candidate solutions that are used as input to the model, whereas the number of sampling steps defines how often we re-use the output during model sampling as input to the model. We study the generalization of the royal tree problem, the Airfoil problem, and the Pagie-1 problem, and find that both corruption strength and the number of sampling steps influence exploration and exploitation in search and affect performance: exploration increases with stronger corruption and lower number of sampling steps. The results indicate that both corruption and sampling steps are key to the success of the DAE-GP: it permits us to balance the exploration and exploitation behavior in search, resulting in an improved search quality. However, also selection is important for exploration and exploitation and should be chosen wisely.	en_GB
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	004 Informatik	de_DE
dc.subject.ddc	004 Data processing	en_GB
dc.subject.ddc	330 Wirtschaft	de_DE
dc.subject.ddc	330 Economics	en_GB
dc.title	Denoising autoencoder genetic programming : strategies to control exploration and exploitation in search	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-9806	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 03 Rechts- und Wirtschaftswissenschaften	de
jgu.organisation.number	2300	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Genetic programming and evolvable machines	de
jgu.journal.volume	24	de
jgu.pages.alternative	17	de
jgu.publisher.year	2023	-
jgu.publisher.name	Springer Science + Business Media B.V.	de
jgu.publisher.place	Dordrecht u.a.	de
jgu.publisher.issn	1573-7632	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	004	de
jgu.subject.ddccode	330	de
jgu.publisher.doi	10.1007/s10710-023-09462-2	de
jgu.organisation.ror	https://ror.org/023b0x485	-
Appears in collections:	DFG-491381577-H

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