A model for the emergence of adaptive subsystems

We investigate the interaction of learning and evolution in a changing environment. A stable learning capability is regarded as an emergent adaptive system evolved by natural selection of genetic variants. We consider the evolution of an asexual population. Each genotype can have 'fixed' a...

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Publicado: 2003
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00928240_v65_n1_p27_Dopazo
http://hdl.handle.net/20.500.12110/paper_00928240_v65_n1_p27_Dopazo
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_00928240_v65_n1_p27_Dopazo
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spelling paper:paper_00928240_v65_n1_p27_Dopazo2023-06-08T15:08:22Z A model for the emergence of adaptive subsystems We investigate the interaction of learning and evolution in a changing environment. A stable learning capability is regarded as an emergent adaptive system evolved by natural selection of genetic variants. We consider the evolution of an asexual population. Each genotype can have 'fixed' and 'flexible' alleles. The former express themselves as synaptic connections that remain unchanged during ontogeny and the latter as synapses that can be adjusted through a learning algorithm. Evolution is modelled using genetic algorithms and the changing environment is represented by two optimal synaptic patterns that alternate a fixed number of times during the 'life' of the individuals. The amplitude of the change is related to the Hamming distance between the two optimal patterns and the rate of change to the frequency with which both exchange roles. This model is an extension of that of Hinton and Nowlan in which the fitness is given by a probabilistic measure of the Hamming distance to the optimum. We find that two types of evolutionary pathways are possible depending upon how difficult (costly) it is to cope with the changes of the environment. In one case the population loses the learning ability, and the individuals inherit fixed synapses that are optimal in only one of the environmental states. In the other case a flexible subsystem emerges that allows the individuals to adapt to the changes of the environment. The model helps us to understand how an adaptive subsystem can emerge as the result of the tradeoff between the exploitation of a congenital structure and the exploration of the adaptive capabilities practised by learning. © 2002 Society for Mathematical Biology. Published by Elsevier Science Ltd. All rights reserved. 2003 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00928240_v65_n1_p27_Dopazo http://hdl.handle.net/20.500.12110/paper_00928240_v65_n1_p27_Dopazo
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
description We investigate the interaction of learning and evolution in a changing environment. A stable learning capability is regarded as an emergent adaptive system evolved by natural selection of genetic variants. We consider the evolution of an asexual population. Each genotype can have 'fixed' and 'flexible' alleles. The former express themselves as synaptic connections that remain unchanged during ontogeny and the latter as synapses that can be adjusted through a learning algorithm. Evolution is modelled using genetic algorithms and the changing environment is represented by two optimal synaptic patterns that alternate a fixed number of times during the 'life' of the individuals. The amplitude of the change is related to the Hamming distance between the two optimal patterns and the rate of change to the frequency with which both exchange roles. This model is an extension of that of Hinton and Nowlan in which the fitness is given by a probabilistic measure of the Hamming distance to the optimum. We find that two types of evolutionary pathways are possible depending upon how difficult (costly) it is to cope with the changes of the environment. In one case the population loses the learning ability, and the individuals inherit fixed synapses that are optimal in only one of the environmental states. In the other case a flexible subsystem emerges that allows the individuals to adapt to the changes of the environment. The model helps us to understand how an adaptive subsystem can emerge as the result of the tradeoff between the exploitation of a congenital structure and the exploration of the adaptive capabilities practised by learning. © 2002 Society for Mathematical Biology. Published by Elsevier Science Ltd. All rights reserved.
title A model for the emergence of adaptive subsystems
spellingShingle A model for the emergence of adaptive subsystems
title_short A model for the emergence of adaptive subsystems
title_full A model for the emergence of adaptive subsystems
title_fullStr A model for the emergence of adaptive subsystems
title_full_unstemmed A model for the emergence of adaptive subsystems
title_sort model for the emergence of adaptive subsystems
publishDate 2003
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00928240_v65_n1_p27_Dopazo
http://hdl.handle.net/20.500.12110/paper_00928240_v65_n1_p27_Dopazo
_version_ 1768543838541971456

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