LEARNING AND EVOLUTION IN NEURAL NETWORKS 


           Stefano Nolfi*   Jeffrey L. Elman+   Domenico Parisi*

                          *Institute of Psychology, 
                   National Research Council, Rome, Italy
                      +Department of Cognitive Science, 
                 University of California, San Diego, La Jolla.


The paper describes simulations on populations  of  neural networks that both 
evolve at the population  level  and learn  at  the  individual level. Unlike 
other  simulations,  the  evolutionary task (finding food in the environment)
and the learning task (predicting  the  next position of food on the basis of 
present position  and  planned  network's  movement)  are different tasks. In 
these  conditions  both  learning  influences  evolution  (without Lamarckian 
inheritance of  learned  weight  changes)  and  evolution influences learning.
Average but  not  peak fitness has a better evolutionary growth with learning 
than  without learning. After  the initial generations individuals that learn 
to  predict during  life also improve their food finding ability during life. 
Furthermore, individuals  which  inherit an innate capacity to find food also 
inherit an innate predisposition to learn to predict the sensory consequences 
of their movements. They do not predict better  at birth but they do learn to 
predict better  than  individuals  of  the  initial generation given the same 
learning experience.  The results  are  interpreted  in  terms of a notion of 
dynamic  correlation  between  the  fitness surface and the learning surface. 
Evolution succeeds  in  finding  both  individuals that have high fitness and 
individuals  that  although  they  do  not  have high fitness at birth end up 
with high fitness because of learning to predict.