<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">H. Dopazo</style></author><author><style face="normal" font="default" size="100%">Gordon, M. B.</style></author><author><style face="normal" font="default" size="100%">Perazzo, R.</style></author><author><style face="normal" font="default" size="100%">Risau-Gusman, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A model for the emergence of adaptive subsystems</style></title><secondary-title><style face="normal" font="default" size="100%">Bull Math Biol</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">*Adaptation</style></keyword><keyword><style  face="normal" font="default" size="100%">Biological Algorithms Alleles Animals Evolution Genotype Humans *Learning *Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical Neural Networks (Computer) Phenotype Synapses/genetics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=12597115</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">65</style></volume><pages><style face="normal" font="default" size="100%">27-56</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><notes><style face="normal" font="default" size="100%">Dopazo, H Gordon, M B Perazzo, R Risau-Gusman, S Research Support, Non-U.S. Gov’t United States Bulletin of mathematical biology Bull Math Biol. 2003 Jan;65(1):27-56.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">H. Dopazo</style></author><author><style face="normal" font="default" size="100%">Gordon, M. B.</style></author><author><style face="normal" font="default" size="100%">Perazzo, R.</style></author><author><style face="normal" font="default" size="100%">Risau-Gusman, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A model for the interaction of learning and evolution</style></title><secondary-title><style face="normal" font="default" size="100%">Bull Math Biol</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms Alleles Animals *Evolution Genotype Humans *Learning *Neural Networks (Computer) Numerical Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer-Assisted Phenotype Synapses/genetics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=11146879</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">63</style></volume><pages><style face="normal" font="default" size="100%">117-34</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a simple model in order to discuss the interaction of the genetic and behavioral systems throughout evolution. This considers a set of adaptive perceptrons in which some of their synapses can be updated through a learning process. This framework provides an extension of the well-known Hinton and Nowlan model by blending together some learning capability and other (rigid) genetic effects that contribute to the fitness. We find a halting effect in the evolutionary dynamics, in which the transcription of environmental data into genetic information is hindered by learning, instead of stimulated as is usually understood by the so-called Baldwin effect. The present results are discussed and compared with those reported in the literature. An interpretation is provided of the halting effect.</style></abstract><notes><style face="normal" font="default" size="100%">Dopazo, H Gordon, M B Perazzo, R Risau-Gusman, S Comparative Study Research Support, Non-U.S. Gov’t United States Bulletin of mathematical biology Bull Math Biol. 2001 Jan;63(1):117-34.</style></notes></record></records></xml>