Promoter based genetic algorithm - Revision history

Geysirhead: removed Category:Evolutionary computation using HotCat not needed

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	{{Evolutionary computation}}		{{Evolutionary computation}}
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Flod logic: Adding short description: "Genetic algorithm for neuroevolution"

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Adding short description: "Genetic algorithm for neuroevolution"

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			{{Short description\|Genetic algorithm for neuroevolution}}
	{{Evolutionary algorithms}}		{{Evolutionary algorithms}}
	'''The promoter based genetic algorithm''' ('''PBGA''') is a [[genetic algorithm]] for neuroevolution developed by F. Bellas and R.J. Duro in the [[Integrated Group for Engineering Research]] (GII) at the University of Coruña, in Spain. It evolves variable size feedforward [[artificial neural network]]s (ANN) that are encoded into sequences of genes for constructing a basic ANN unit. Each of these blocks is preceded by a gene promoter acting as an on/off switch that determines if that particular unit will be expressed or not.		'''The promoter based genetic algorithm''' ('''PBGA''') is a [[genetic algorithm]] for neuroevolution developed by F. Bellas and R.J. Duro in the [[Integrated Group for Engineering Research]] (GII) at the University of Coruña, in Spain. It evolves variable size feedforward [[artificial neural network]]s (ANN) that are encoded into sequences of genes for constructing a basic ANN unit. Each of these blocks is preceded by a gene promoter acting as an on/off switch that determines if that particular unit will be expressed or not.

Jarble: adding Template:Evolutionary computation

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	*[https://archive.today/20130106133654/http://www.gii.udc.es/richard_duro Richard J. Duro’s website]		*[https://archive.today/20130106133654/http://www.gii.udc.es/richard_duro Richard J. Duro’s website]

			{{Evolutionary computation}}
	[[Category:Artificial neural networks]]		[[Category:Artificial neural networks]]
	[[Category:Evolutionary algorithms]]		[[Category:Evolutionary algorithms]]

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External links: archive link repair, may include: archive.* -> archive.today, and http->https for ghostarchive.org and archive.org (wp:el#Specifying_protocols)

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	==External links==		==External links==
	*[http://www.gii.udc.es Grupo Integrado de Ingeniería]		*[http://www.gii.udc.es Grupo Integrado de Ingeniería]
	*[https://archive.is/20130106140625/http://www.gii.udc.es/francisco_bellas Francisco Bellas’ website]		*[https://archive.today/20130106140625/http://www.gii.udc.es/francisco_bellas Francisco Bellas’ website]
	*[https://archive.is/20130106133654/http://www.gii.udc.es/richard_duro Richard J. Duro’s website]		*[https://archive.today/20130106133654/http://www.gii.udc.es/richard_duro Richard J. Duro’s website]

	[[Category:Artificial neural networks]]		[[Category:Artificial neural networks]]

Jarble: adding links to references using Google Scholar

2019-08-31T16:25:06Z

adding links to references using Google Scholar

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	==Results ==		==Results ==

	The PBGA was originally presented<ref name="bellasIASTED">F. Bellas, R. J. Duro, (2002) Statistically neutral promoter based GA for evolution with dynamic fitness functions, Proc. of IASTED International Conference Artificial Intelligence and Applications</ref><ref name="bellasICONIP">F. Bellas, R. J. Duro, (2002) Modelling the world with statiscally neutral PBGAs. Enhancement and real applications, Proc. 9th Internacional Conference on Neural Information Processing</ref> within the field of autonomous robotics, in particular in the real time learning of environment models of the robot.		The PBGA was originally presented<ref name="bellasIASTED">F. Bellas, R. J. Duro, (2002) [http://www.actapress.com/pdfviewer.aspx?paperid=25656 Statistically neutral promoter based GA for evolution with dynamic fitness functions], Proc. of IASTED International Conference Artificial Intelligence and Applications</ref><ref name="bellasICONIP">F. Bellas, R. J. Duro, (2002) [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.706.1897&rep=rep1&type=pdf Modelling the world with statiscally neutral PBGAs]. Enhancement and real applications, Proc. 9th Internacional Conference on Neural Information Processing</ref> within the field of autonomous robotics, in particular in the real time learning of environment models of the robot.

	It has been used inside the [[Multilevel Darwinist Brain]] (MDB) cognitive mechanism developed in the GII for real robots on-line learning. In another paper<ref name="bellas06">F. Bellas, A. Faiña, A. Prieto, and R.J. Duro (2006), Adaptive Learning Application of the MDB Evolutionary Cognitive Architecture in Physical Agents, Lecture notes on artificial intelligence, vol 4095, 434-445</ref> it is shown how the application of the PBGA together with an external memory that stores the successful obtained world models, is an optimal strategy for adaptation in dynamic environments.		It has been used inside the [[Multilevel Darwinist Brain]] (MDB) cognitive mechanism developed in the GII for real robots on-line learning. In another paper<ref name="bellas06">F. Bellas, A. Faiña, A. Prieto, and R.J. Duro (2006), [https://link.springer.com/chapter/10.1007/11840541_36 Adaptive Learning Application of the MDB Evolutionary Cognitive Architecture in Physical Agents], Lecture notes on artificial intelligence, vol 4095, 434-445</ref> it is shown how the application of the PBGA together with an external memory that stores the successful obtained world models, is an optimal strategy for adaptation in dynamic environments.

	Recently, the PBGA has provided results that outperform other neuroevolutionary algorithms in non-stationary problems, where the fitness function varies in time.<ref name="bellas09">F. Bellas, J.A. Becerra, R. J. Duro, (2009), Using Promoters and Functional Introns in Genetic Algorithms for Neuroevolutionary Learning in Non-Stationary Problems, Neurocomputing 72, 2134-2145</ref>		Recently, the PBGA has provided results that outperform other neuroevolutionary algorithms in non-stationary problems, where the fitness function varies in time.<ref name="bellas09">F. Bellas, J.A. Becerra, R. J. Duro, (2009), [https://www.sciencedirect.com/science/article/pii/S0925231208005481 Using Promoters and Functional Introns in Genetic Algorithms for Neuroevolutionary Learning in Non-Stationary Problems], Neurocomputing 72, 2134-2145</ref>

	==References==		==References==

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			{{Evolutionary algorithms}}
	'''The promoter based genetic algorithm''' ('''PBGA''') is a [[genetic algorithm]] for neuroevolution developed by F. Bellas and R.J. Duro in the [[Integrated Group for Engineering Research]] (GII) at the University of Coruña, in Spain. It evolves variable size feedforward [[artificial neural network]]s (ANN) that are encoded into sequences of genes for constructing a basic ANN unit. Each of these blocks is preceded by a gene promoter acting as an on/off switch that determines if that particular unit will be expressed or not.		'''The promoter based genetic algorithm''' ('''PBGA''') is a [[genetic algorithm]] for neuroevolution developed by F. Bellas and R.J. Duro in the [[Integrated Group for Engineering Research]] (GII) at the University of Coruña, in Spain. It evolves variable size feedforward [[artificial neural network]]s (ANN) that are encoded into sequences of genes for constructing a basic ANN unit. Each of these blocks is preceded by a gene promoter acting as an on/off switch that determines if that particular unit will be expressed or not.

InternetArchiveBot: Rescuing 2 sources and tagging 0 as dead. #IABot (v1.6.5)

2018-04-02T06:57:19Z

Rescuing 2 sources and tagging 0 as dead. #IABot (v1.6.5)

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	==External links==		==External links==
	*[http://www.gii.udc.es Grupo Integrado de Ingeniería]		*[http://www.gii.udc.es Grupo Integrado de Ingeniería]
	*[http://www.gii.udc.es/francisco_bellas Francisco Bellas’ website]		*[https://archive.is/20130106140625/http://www.gii.udc.es/francisco_bellas Francisco Bellas’ website]
	*[http://www.gii.udc.es/richard_duro Richard J. Duro’s website]		*[https://archive.is/20130106133654/http://www.gii.udc.es/richard_duro Richard J. Duro’s website]

	[[Category:Artificial neural networks]]		[[Category:Artificial neural networks]]

JaviPrieto: /* Results */ References

2016-11-20T17:43:13Z

Results: References

← Previous revision		Revision as of 17:43, 20 November 2016
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	==Results ==		==Results ==

	The PBGA was originally presented in <ref name="bellasIASTED">F. Bellas, R. J. Duro, (2002) Statistically neutral promoter based GA for evolution with dynamic fitness functions, Proc. of IASTED International Conference Artificial Intelligence and Applications</ref> ~~and~~ <ref name="bellasICONIP">F. Bellas, R. J. Duro, (2002) Modelling the world with statiscally neutral PBGAs. Enhancement and real applications, Proc. 9th Internacional Conference on Neural Information Processing</ref> within the field of autonomous robotics, in particular in the real time learning of environment models of the robot.		The PBGA was originally presented<ref name="bellasIASTED">F. Bellas, R. J. Duro, (2002) Statistically neutral promoter based GA for evolution with dynamic fitness functions, Proc. of IASTED International Conference Artificial Intelligence and Applications</ref><ref name="bellasICONIP">F. Bellas, R. J. Duro, (2002) Modelling the world with statiscally neutral PBGAs. Enhancement and real applications, Proc. 9th Internacional Conference on Neural Information Processing</ref> within the field of autonomous robotics, in particular in the real time learning of environment models of the robot.

	It has been used inside the [[Multilevel Darwinist Brain]] (MDB) cognitive mechanism developed in the GII for real robots on-line learning. In ~~the~~ paper <ref name="bellas06">F. Bellas, A. Faiña, A. Prieto, and R.J. Duro (2006), Adaptive Learning Application of the MDB Evolutionary Cognitive Architecture in Physical Agents, Lecture notes on artificial intelligence, vol 4095, 434-445</ref> it is shown how the application of the PBGA together with an external memory that stores the successful obtained world models, is an optimal strategy for adaptation in dynamic environments.		It has been used inside the [[Multilevel Darwinist Brain]] (MDB) cognitive mechanism developed in the GII for real robots on-line learning. In another paper<ref name="bellas06">F. Bellas, A. Faiña, A. Prieto, and R.J. Duro (2006), Adaptive Learning Application of the MDB Evolutionary Cognitive Architecture in Physical Agents, Lecture notes on artificial intelligence, vol 4095, 434-445</ref> it is shown how the application of the PBGA together with an external memory that stores the successful obtained world models, is an optimal strategy for adaptation in dynamic environments.

	Recently, the PBGA has provided results that outperform other neuroevolutionary algorithms in non-stationary problems, where the fitness function varies in time.<ref name="bellas09">F. Bellas, J.A. Becerra, R. J. Duro, (2009), Using Promoters and Functional Introns in Genetic Algorithms for Neuroevolutionary Learning in Non-Stationary Problems, Neurocomputing 72, 2134-2145</ref>		Recently, the PBGA has provided results that outperform other neuroevolutionary algorithms in non-stationary problems, where the fitness function varies in time.<ref name="bellas09">F. Bellas, J.A. Becerra, R. J. Duro, (2009), Using Promoters and Functional Introns in Genetic Algorithms for Neuroevolutionary Learning in Non-Stationary Problems, Neurocomputing 72, 2134-2145</ref>

Glenn: catspec

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← Previous revision		Revision as of 19:50, 1 June 2014
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	*[http://www.gii.udc.es/richard_duro Richard J. Duro’s website]		*[http://www.gii.udc.es/richard_duro Richard J. Duro’s website]

	[[Category:~~Neural~~ networks]]		[[Category:Artificial neural networks]]
	[[Category:Evolutionary algorithms]]		[[Category:Evolutionary algorithms]]
	[[Category:Evolutionary computation]]		[[Category:Evolutionary computation]]