Evolution and learning in neural networks: the number and distribution

Abstract

The present invention relates to the interrelationships between nature (as mediated by evolution and genetic algorithms) and nurture (as mediated by gradient-descent supervised learning) in a population of neural networks for pattern recognition. The Baldwin effect is demonstrated that learning can change the rate of evolution of the population's genome - a 'pseudo-Lamarkian' process, in which information learned is ultimately encoded in the genome by a purely Darwinian process. Selectivity is shown for this effect: too much learning or too little learning in each generation leads to slow evolution of the genome, whereas an intermediate amount leads to most rapid evolution. For a given number of learning trials throughout a population, the most rapid evolution occurs if different individuals each receive a different number of learning trials, rather than the same number. Because all biological networks possess structure due to evolution, it is important that such interactions between learning and evolution be understood. Hybrid systems can take advantage both of gradient descents (learning) and large jumps (genetic algorithms) in very complicated energy landscapes and hence may play an increasingly important role in the design of artificial neural systems.