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[stefano nolfi]

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                  Learning, Behavior, and Evolution

    Domenico Parisi          Stefano Nolfi          Federico Cecconi
                       Institute of Psychology
                             CNR - Rome
                    e-mail: stiva at irmkant.Bitnet


                               Abstract

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.


This paper will be presented at ECAL-91 - European Conference on Artificial
Life, December 1991, Paris.