02406nas a2200205   4500008004100000245005300041210005100094300001000145490000700155520166900162653001601831653008601847653001901933653007101952100001502023700001902038700001602057700002102073856010602094       2003                        eng d00aA model for the emergence of adaptive subsystems0 amodel for the emergence of adaptive subsystems  a27-560 v653 aWe 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.10a*Adaptation10aBiological Algorithms Alleles Animals Evolution Genotype Humans *Learning *Models10aGenetic Models10aStatistical Neural Networks (Computer) Phenotype Synapses/genetics1 aDopazo, H.1 aGordon, M., B.1 aPerazzo, R.1 aRisau-Gusman, S.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1259711501507nas a2200181   4500008004100000245005800041210005600099300001100155490000700166520081000173653011500983653005001098100001501148700001901163700001601182700002101198856010601219       2001                        eng d00aA model for the interaction of learning and evolution0 amodel for the interaction of learning and evolution  a117-340 v633 aWe 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.10aAlgorithms Alleles Animals *Evolution Genotype Humans *Learning *Neural Networks (Computer) Numerical Analysis10aComputer-Assisted Phenotype Synapses/genetics1 aDopazo, H.1 aGordon, M., B.1 aPerazzo, R.1 aRisau-Gusman, S.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11146879