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.
Dopazo
H
H
Departamento de Biología, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Pabellón 2, Ciudad Universitaria, 1428 Buenos Aires, Argentina. hdopazo@dna.uba.ar
Gordon
M B
MB
Perazzo
R
R
Risau-Gusman
S
S
D000220:Adaptation, Biological; D000465:Algorithms; D000483:Alleles; D000818:Animals; D005075:Biological Evolution; D005838:Genotype; D006801:Humans; D007858:Learning; D008957:Models, Genetic; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010641:Phenotype; D013569:Synapses / Q000235:genetics