Q-Learning Induced Artificial Bee Colony for Noisy Optimization

The paper proposes a novel approach to adaptive selection of sample size for a trial solution of an evolutionary algorithm when noise of unknown distribution contaminates the objective surface. The sample size of a solution here is adapted based on the noisy fitness profile in the local surrounding...

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Detalles Bibliográficos
Autores: Rakshit, P., Konar, A., Nagar, A.K.
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2020
País:España
Institución:Basque Center for Applied Mathematics (BCAM)
Repositorio:BIRD. BCAM's Institutional Repository Data
OAI Identifier:oai:bird.bcamath.org:20.500.11824/1235
Acceso en línea:http://hdl.handle.net/20.500.11824/1235
Access Level:acceso abierto
Palabra clave:artificial bee colony
noise-handling
temporal difference Q-learning
reinforcement learning
sampling
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spelling Q-Learning Induced Artificial Bee Colony for Noisy OptimizationRakshit, P.Konar, A.Nagar, A.K.artificial bee colonynoise-handlingtemporal difference Q-learningreinforcement learningsamplingThe paper proposes a novel approach to adaptive selection of sample size for a trial solution of an evolutionary algorithm when noise of unknown distribution contaminates the objective surface. The sample size of a solution here is adapted based on the noisy fitness profile in the local surrounding of the given solution. The fitness estimate and the fitness variance of a sub-population surrounding the given solution are jointly used to signify the degree of noise contamination in its local neighborhood (LN). The adaptation of sample size based on the characteristics of the fitness landscape in the LN of a solution is realized here with the temporal difference Q-learning (TDQL). The merit of the present work lies in utilizing the reward-penalty based reinforcement learning mechanism of TDQL for sample size adaptation. This sidesteps the prerequisite setting of any specific functional form of relationship between the sample size requirement of a solution and the noisy fitness profile in its LN. Experiments undertaken reveal that the proposed algorithms, realized with artificial bee colony, significantly outperform the existing counterparts and the state-of-the-art algorithms.202120212020info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttp://hdl.handle.net/20.500.11824/1235reponame:BIRD. BCAM's Institutional Repository Datainstname:Basque Center for Applied Mathematics (BCAM)InglésReconocimiento-NoComercial-CompartirIgual 3.0 Españahttp://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:bird.bcamath.org:20.500.11824/12352026-06-19T12:47:47Z
dc.title.none.fl_str_mv Q-Learning Induced Artificial Bee Colony for Noisy Optimization
title Q-Learning Induced Artificial Bee Colony for Noisy Optimization
spellingShingle Q-Learning Induced Artificial Bee Colony for Noisy Optimization
Rakshit, P.
artificial bee colony
noise-handling
temporal difference Q-learning
reinforcement learning
sampling
title_short Q-Learning Induced Artificial Bee Colony for Noisy Optimization
title_full Q-Learning Induced Artificial Bee Colony for Noisy Optimization
title_fullStr Q-Learning Induced Artificial Bee Colony for Noisy Optimization
title_full_unstemmed Q-Learning Induced Artificial Bee Colony for Noisy Optimization
title_sort Q-Learning Induced Artificial Bee Colony for Noisy Optimization
dc.creator.none.fl_str_mv Rakshit, P.
Konar, A.
Nagar, A.K.
author Rakshit, P.
author_facet Rakshit, P.
Konar, A.
Nagar, A.K.
author_role author
author2 Konar, A.
Nagar, A.K.
author2_role author
author
dc.subject.none.fl_str_mv artificial bee colony
noise-handling
temporal difference Q-learning
reinforcement learning
sampling
topic artificial bee colony
noise-handling
temporal difference Q-learning
reinforcement learning
sampling
description The paper proposes a novel approach to adaptive selection of sample size for a trial solution of an evolutionary algorithm when noise of unknown distribution contaminates the objective surface. The sample size of a solution here is adapted based on the noisy fitness profile in the local surrounding of the given solution. The fitness estimate and the fitness variance of a sub-population surrounding the given solution are jointly used to signify the degree of noise contamination in its local neighborhood (LN). The adaptation of sample size based on the characteristics of the fitness landscape in the LN of a solution is realized here with the temporal difference Q-learning (TDQL). The merit of the present work lies in utilizing the reward-penalty based reinforcement learning mechanism of TDQL for sample size adaptation. This sidesteps the prerequisite setting of any specific functional form of relationship between the sample size requirement of a solution and the noisy fitness profile in its LN. Experiments undertaken reveal that the proposed algorithms, realized with artificial bee colony, significantly outperform the existing counterparts and the state-of-the-art algorithms.
publishDate 2020
dc.date.none.fl_str_mv 2020
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.11824/1235
url http://hdl.handle.net/20.500.11824/1235
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:BIRD. BCAM's Institutional Repository Data
instname:Basque Center for Applied Mathematics (BCAM)
instname_str Basque Center for Applied Mathematics (BCAM)
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collection BIRD. BCAM's Institutional Repository Data
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