LEARNING, BEHAVIOR AND EVOLUTION

We present simulations of evolutionary processes operating on populations of 
neural networks to show  how learning  and behavior  can influence evolution 
within   a   strictly  Darwinian  framework. Learning   can   accelerate the 
evolutionary process both when  learning  tasks correlated  with the fitness 
criterion and when random learning tasks are used. Furthermore,  an  ability
to  learn  a  task  can  emerge  and  be transmitted evolutionarily for both 
correlated  and  uncorrelated  tasks.  Finally,  behavior  that  allows  the 
individual  to  self-select  the incoming stimuli can influence evolution by 
becoming one of the factors that determine  the  observed phenotypic fitness
on which selective reproduction  is based. For all the effects demonstrated, 
we advance a consistent explanation  in  terms  of a multidimensional weight
space for  neural networks,  a  fitness surface  for  the evolutionary task, 
and a performance surface for the learning task.