Learning to adapt to changing environments in evolving neural 
networks

Stefano Nolfi & Domenico Parisi


In order to study learning as an adaptive process it is necessary to 
take into consideration  the  role of evolution which is the primary 
adaptive  process.  In  addition,  learning  should  be  studied  in 
(artificial)  organisms   that   live  in  an  independent  physical 
environment  in  such  a  way  that  the  input from the environment 
can be at least partially controlled by  the organisms' behavior. To 
explore  these  issues  we  used a genetic algorithm to simulate the 
evolution  of  a  population of neural networks each controlling the 
behavior  of  a  small mobile robot that must explore efficiently an 
environment  surrounded  by  walls.  Since  the  environment changes 
from  one  generation  to  the  next  each network must learn during 
its  life  to  adapt  to the particular environment it happens to be 
born in. We found  that  evolved  networks incorporate a genetically 
inherited predisposition  to learn that can be described as: (a) the 
presence of initial conditions that tend to canalize learning in the 
right directions; (b) the  tendency to behave in a way that enhances 
the  perceived  differences  between   different   environments  and 
determines input stimuli  that  facilitate  the learning of adaptive 
changes, and (c) the ability to reach desirable stable states.